This report is step one of a three step process for data organization in the RESPOND study. The three steps include:

  1. Data processing: investigation and reporting
    • Evaluate the data received from the SEER registries and scantron surveys
    • Understand the the variables, their values, distributions, and identify potential problems
    • From this report we can propose potential solutions on how to handle missing or unknown data, alternative values, or duplicated or problematic records
  2. Data cleaning
    • Implement solutions for data cleaning given decisions from step 1 report
    • Keep data in detailed format
    • Create composite variables
  3. Data creation
    • Output data to an “analysis ready” R data set
    • Output data to a file that facilitates input into SAS


The working dataset used in this report results in a database join of all Scantron Surveys currently processed with all Cancer Registry files received to date from study centers, which were able to be linked based on available identifiers.
- This working dataset was created on 2.8.2020
- The dataset includes 3557 records
- The dataset includes 532 variables
- Variables include: registryid, naaccrrecordversion, tumorrecordnumber, addratdxstate, countyatdx, countyatdxgeocode2000, countyatdxgeocode2010, censustract2000, censustract2010, maritalstatusatdx, race1, race2, race3, race4, race5, spanishhispanicorigin, nhiaderivedhisporigin, ihslink, racenapiia, sex, ageatdiagnosis, dateofbirth, birthplace, birthplacestate, birthplacecountry, censusblockgroup2000, censusblockgroup2010, censustrcertainty2000, censustrcertainty2010, sequencenumbercentral, dateofdiagnosis, primarysite, grade, diagnosticconfirmation, typeofreportingsource, histologictypeicdo3, behaviorcodeicdo3, primarypayeratdx, seersummarystage2000, seersummarystage1977, derivedsummarystage2018, summarystage2018, eodprimarytumor, eodregionalnodes, eodmets, derivedeod2018t, eodextension, derivedeod2018m, eodextensionprostpath, eodlymphnodeinvolv, derivedeod2018n, derivedeod2018stagegroup, regionalnodespositive, regionalnodesexamined, tnmpatht, tnmpathn, tnmpathm, tnmpathstagegroup, tnmclint, tnmclinn, tnmclinm, tnmclinstagegroup, ajcctnmclint, ajcctnmclinn, ajcctnmclinm, ajcctnmclinstagegroup, ajcctnmpatht, ajcctnmpathn, ajcctnmpathm, ajcctnmpathstagegroup, tumormarker2, rxdatesurgery, rxdateradiation, rxdatechemo, rxdatehormone, rxdatebrm, rxdateother, dateinitialrxseer, rxdatedxstgproc, rxsummtreatmentstatus, rxsummsurgprimsite, rxsummscopereglnsur, rxsummsurgothregdis, reasonfornosurgery, rxsummradiation, rxsummsurgradseq, rxsummchemo, rxsummhormone, rxsummbrm, rxsummother, radregionaldosecgy, radregionalrxmodality, rxsummsystemicsurseq, rxsummsurgsite9802, rxsummscopereg9802, rxsummsurgoth9802, dateoflastcontact, vitalstatus, survdateactivefollowup, survdatepresumedalive, survdatedxrecode, causeofdeath, csextension, cslymphnodes, csmetsatdx, cssitespecificfactor7, cssitespecificfactor8, cssitespecificfactor9, cssitespecificfactor10, cssitespecificfactor11, cssitespecificfactor12, cssitespecificfactor13, cssitespecificfactor14, cssitespecificfactor15, cssitespecificfactor1, cssitespecificfactor2, cssitespecificfactor3, cssitespecificfactor4, cssitespecificfactor5, cssitespecificfactor6, derivedajcc6t, derivedajcc6n, derivedajcc6m, derivedajcc6stagegrp, derivedss1977, derivedss2000, comorbidcomplication1, comorbidcomplication2, comorbidcomplication3, comorbidcomplication4, comorbidcomplication5, comorbidcomplication6, comorbidcomplication7, comorbidcomplication8, comorbidcomplication9, comorbidcomplication10, icdrevisioncomorbid, rxdatemostdefinsurg, radboostrxmodality, radboostdosecgy, rxdatesystemic, rxsummtransplntendocr, derivedajcc7t, derivedajcc7n, derivedajcc7m, derivedajcc7stagegrp, derivedseerpathstggrp, derivedseerclinstggrp, derivedseercmbstggrp, derivedseercombinedt, derivedseercombinedn, derivedseercombinedm, secondarydiagnosis1, secondarydiagnosis2, secondarydiagnosis3, secondarydiagnosis4, secondarydiagnosis5, secondarydiagnosis6, secondarydiagnosis7, secondarydiagnosis8, secondarydiagnosis9, secondarydiagnosis10, gleasonpatternsclinical, gleasonpatternspathological, gleasonscoreclinical, gleasonscorepathological, gleasontertiarypattern, gradeclinical, gradepathological, numberofcoresexamined, numberofcorespositive, prostatepathologicalextension, psalabvalue, rid, recno, siteid, surveyid, locationname, respondid, methodology, a1month, a1year, a1not, a2, a3_1, a3_2, a3_3, a3_4, a3_5, a3_6, a3_7, a3_8, a3_9, a3_10, a3_11, a3_12, a3_13, a3_14, a3_15, a3_16, a3_17, a3_18, a3_19, a3_20, a3_21, a3_22, a3_23, a3_24, a3other, a4month, a4year, a5, a5other, a6, a6other, a7, a7other, a8, b1aa, b1ab, b1ac, b1bno, b1ba, b1ba2, b1bb, b1bc, b1cno, b1ca, b1ca2, b1cb, b1cc, b1da, b1db, b1dc, b1ea, b1eb, b1ec, b2, b2a_1, b2a_2, b2a_3, b2a_4, b2a_5, b2a_6, b2a_7, b2b_1, b2b_3, b2b_4, b2b_5, b2b_6, b2b_7, b2cno, b2c_1, b2c_2, b2c_3, b2c_4, b2c_5, b2c_6, b2c_7, b2dno, b2d_1, b2d_3, b2d_4, b2d_5, b2d_6, b2d_7, b2eno, b2e_1, b2e_2, b2e_3, b2e_4, b2e_5, b2e_6, b2e_7, b2fno, b2f_1, b2f_3, b2f_4, b2f_5, b2f_6, b2f_7, b3, b4aa, b4ab, b4ba, b4bb, b4ca, b4cb, b4da, b4db, b4dc, b4ea, b4eb, b4fa, b4fb, b4fc, b4ga, b4gb, b4ha, b4hb, b4ia, b4ib, b4ja, b4jb, b4jc, b4jd, b4ka, b4kb, b4la, b4lb, b4ma, b4mb, b4na, b4nb, b4oa, b4ob, b4pa, b4pb, b4qa, b4qb, b4qother, b5, b5other, c1, c2a1, c2a2, c2a3, c2b1, c2b2, c2b3, c2c1, c2c2, c2c3, c3a1, c3a2, c3a3, c3b1, c3b2, c3b3, c3c1, c3c2, c3c3, c3d1, c3d2, c3d3, c4a1, c4a2, c4a3, c4b1, c4b2, c4b3, c4c1, c4c2, c4c3, c4d1, c4d2, c4d3, c4e1, c4e2, c4e3, d1aa, d1ab, d1ba, d1bb, d1ca, d1cb, d1da, d1db, d1ea, d1eb, d1fa, d1fb, d1ga, d1gb, d2a, d2b, d2c, d2d, d2e, d3a1, d3a2, d3a3, d3b1, d3b2, d3b3, d3c1, d3c2, d3c3, d3d1, d3d2, d3d3, d3e1, d3e2, d3e3, d3f1, d3f2, d3f3, d3g1, d3g2, d3g3, d3h1, d3h2, d3h3, d3i1, d3i2, d3i3, d3j1, d3j2, d3j3, d4a, d4b, d4c, d4d, d4e, d4f, d4g, d4h, d4i, d4j, d4k, d4l, d5a, d5b, d5c, d5d, d5e, d5f, d5g, d5h, d5i, d5j, d5k, e1_1, e1_2, e1_3, e1_4, e1_5, e1_6, e1other, e2aa, e2ab, e2ba, e2bb, e3, e4, e5, e6, e7, e8, e9_1, e9_2, e9_3, e9_4, e9_5, e9_6, e9_7, e9_8, e9_9, e9_10, e10_1, e10_2, e10_3, e10_4, e10_5, e10_6, e10_7, e10_8, e10_3_1, e10_3_2, e10_3_3, e10_4_1, e10_4_2, e10_4_3, e10_5_1, e10_5_2, e10_5_3, e10_5_4, e10_5_5, e11a, e11b, e11c, e11d, e11e, e11f, e12, e13, e14, e15, f1ft, f1in, f1cm, f2lbs, f2kgs, f3, f4, f5, f6, f7, f7age, f7a, f7b, f7bage, g1, g2_1, g2_2, g2_3, g2_4, g2_5, g3, g3other, g4a, g4b, g4c, g5, g5other, g6_1, g6_2, g6_3, g6_4, g6_5, g6_6, g6_7, g6_8, g7, g8, g9a, g9b, g9c, g10, g10other, g11, g12


SITE ID

  • Codes
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit


  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  # NEED REGISTRY NAMES!!
 
  
  #replace number with names
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  
  
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  
  #cro(new.d$siteid) # this is pretty but doesn't show NAs
  #summary(new.d$siteid)
  
  #Using kable function to form a nice table
  
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of 7 Registries")
Overview of 7 Registries
Registry Total
Greater CA.10 315
Georgia.20 1754
Northern CA.30 210
Louisiana.40 585
Detroit.60 356
Michigan.61 16
Los Angeles County.80 321


NAACCR RECORD VERSION

  • Description: This item applies only to record types I, C, A, and M. Code the NAACCR record version used to create the record. The correction record (U) has its own record version data item.
  • Rationale: The NAACCR Layout version is necessary to communicate to the recipient of data in NAACCR form where the various items are found and how they are coded. It should be added to the record when the recorded is created.
  • Codes
    • 120 2010 Version 12
    • 121 2011 Version 12.1
    • 122 2012 Version 12.2
    • 130 2013 Version 13
    • 140 2014 Version 14
    • 150 2015 Version 15
    • 160 2016 Version 16
    • 180 2018 Version 18
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#50


  naaccrrecordversion <- as.factor(trimws(d[,"naaccrrecordversion"]))
  
  
  levels(naaccrrecordversion)[levels(naaccrrecordversion)=="180"] <- "2018_Version_18.180"

  
  new.d <- data.frame(new.d, naaccrrecordversion)
  new.d <- apply_labels(new.d, naaccrrecordversion = "naaccr Record Version")
  new.d.1 <- data.frame(new.d.1, naaccrrecordversion)
  
  #cro(new.d$siteid) # this is pretty but doesn't show NAs
  #summary(new.d$siteid)
  
  #Using kable function to form a nice table
  
  naaccrrecordversion<-count(new.d$naaccrrecordversion)
  colnames(naaccrrecordversion)<- c("Version", "Total")
  kable(naaccrrecordversion, format = "simple", align = 'l', caption = "Overview of Version")
Overview of Version
Version Total
2018_Version_18.180 3557


TUMOR RECORD NUMBER

  • Description: A system-generated number assigned to each tumor. The number should never change even if the tumor sequence is changed or a record (tumor) is deleted.
  • Rationale: This is a unique number that identifies a specific tumor so data can be linked. “Sequence Number” cannot be used as a link because the number is changed if a report identifies an earlier tumor or if a tumor record is deleted.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#60


All data

st_css() #IMPORTANT!
  tumorrecordnumber <- as.factor(trimws(d[,"tumorrecordnumber"]))
  
  new.d <- data.frame(new.d, tumorrecordnumber)
  new.d <- apply_labels(new.d, tumorrecordnumber = "tumor record number")
  new.d.1 <- data.frame(new.d.1, tumorrecordnumber)
  
  #Using kable function to form a nice table

  temp.d <- data.frame (new.d.1, tumorrecordnumber)
  summarytools::view(dfSummary(new.d$tumorrecordnumber, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tumorrecordnumber [labelled, factor] tumor record number 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
604(18.5%)
328(10.0%)
6(0.2%)
2186(66.9%)
130(4.0%)
13(0.4%)
3(0.1%)
 287 (8.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
304(94.7%)
0(0.0%)
0(0.0%)
5(1.6%)
10(3.1%)
2(0.6%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
0(0.0%)
0(0.0%)
0(0.0%)
200(95.2%)
7(3.3%)
2(1.0%)
1(0.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
300(95.2%)
0(0.0%)
0(0.0%)
2(0.6%)
13(4.1%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
0(0.0%)
180(50.6%)
2(0.6%)
155(43.5%)
15(4.2%)
3(0.8%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
0(0.0%)
0(0.0%)
0(0.0%)
286(96.0%)
10(3.4%)
2(0.7%)
0(0.0%)
 287 (49.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
0(0.0%)
148(8.4%)
4(0.2%)
1523(86.8%)
74(4.2%)
4(0.2%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tumor_record_number [factor] 1. 0 2. 01 3. 02 4. 1 5. 2 6. 3 7. 4
0(0.0%)
0(0.0%)
0(0.0%)
15(93.8%)
1(6.2%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


ADDR AT DX–STATE

  • Description: Identifies the patient’s state or province of residence at the time of diagnosis as identified by the Reporting Source. For consolidated records, the state may be based on reported or corrected residential address information.

  • Rationale: The state of residence is part of the patient’s demographic data and has multiple uses. It can be used to evaluate referral patterns, allows for the analysis of cancer cluster concerns, and supports epidemiological studies that use area-based social measures.

  • Instructions for Coding

    • This field is intended to store residential state for the patient’s physical, residential address. The state for PO Box mailing address should not be entered into this data item except in the infrequent case when no other address information is available.
    • If the patient has multiple tumors, state at diagnosis may be different for each tumor.
    • Do not update this item if the patient’s residential address changes. Store address update information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a state during the geocoding or consolidation process.
    • Use the U.S. Postal Service abbreviation (for the state, territory, commonwealth, U.S. possession) or Canada Post abbreviation (for the Canadian province/territory) in which the patient resides at the time the reportable tumor is diagnosed.
    • If the patient is a foreign resident, then code either XX or YY depending on the circumstance.

  • Codes (in addition to USPS abbreviations)

    • CD Resident of Canada, NOS (province/territory unknown)
    • US Resident of United States, NOS (state/commonwealth/territory/possession unknown)
    • XX Resident of country other than the United States (including its territories, commonwealths, or possessions) or Canada, and country is known
    • YY Resident of country other than the United States (including its territories, commonwealths, or possessions) or Canada, and country is unknown
    • ZZ Residence unknow

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#80


All data

st_css() #IMPORTANT!
  addratdxstate <- as.factor(trimws(d[,"addratdxstate"]))
  
  new.d <- data.frame(new.d, addratdxstate)
  new.d <- apply_labels(new.d, addratdxstate = "addr at dx--state")
  new.d.1 <- data.frame(new.d.1, addratdxstate)
  
  #Using kable function to form a nice table

  temp.d <- data.frame (new.d.1, addratdxstate)
  summarytools::view(dfSummary(new.d$addratdxstate, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 addratdxstate [labelled, factor] addr at dx--state 1. CA 2. GA 3. LA 4. MI
846(23.8%)
1754(49.3%)
585(16.4%)
372(10.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
321(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
210(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
315(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
0(0.0%)
0(0.0%)
0(0.0%)
356(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
0(0.0%)
0(0.0%)
585(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
0(0.0%)
1754(100.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 addr_at_dx_state [factor] 1. CA 2. GA 3. LA 4. MI
0(0.0%)
0(0.0%)
0(0.0%)
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COUNTY AT DX REPORTED

  • Description: Identifies the

  • Description: Code for the county of the patients residence at the time of diagnosis as identified by the Reporting Source. For U.S. residents, standard codes are those of the FIPS publication Counties and Equivalent Entities of the United States, Its Possessions, and Associated Areas or their equivalent INCITS codes.

  • Calculating county and county-based variable rates using this item is not recommended. The more specific, geocoded county items should be used when available.

  • Rationale: This data item may be used for epidemiological purposes. For example, to measure cancer incidence in a particular geographic area.

  • Instructions for Coding

    • This field is intended to store address information for the patient’s physical, residential address. All efforts should be made to find the patient’s true street address and postal code, including reviewing relevant sources outside the medical record if available. The county for a PO Box mailing address should only be recorded when no other address information is available in the medical record and no other information sources are available.
    • If the patient has multiple tumors, county at diagnosis may be different for each tumor.
    • Do not update this item if the patient’s county of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct county during a consolidation process.
    • This variable is coded at time of abstracting and is considered less accurate than the derived, geocoded county at diagnosis variables: County at Diagnosis 1990, 2000, 2010, & 2020.
    • Detailed standards have not been set for Canadian provinces/territories. Use code 998 for Canadian residents.

  • Codes (in addition to FIPS and Geocodes)

    • 001-997 Valid FIPS code
    • 998 Known town, city, state, or country of residence but county code not known AND a resident outside of the state of reporting institution (must meet all criteria). Use this code for Canadian residents.
    • 999 The county of the patient is unknown, or the patient is not a United States resident. County is not documented in the patient’s medical record.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#90

All data

st_css() #IMPORTANT!
  countyatdx <- as.factor(trimws(d[,"countyatdx"]))
  
  new.d <- data.frame(new.d, countyatdx)
  new.d <- apply_labels(new.d, countyatdx = "addr at dx--state")
  new.d.1 <- data.frame(new.d.1, countyatdx)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, countyatdx)
  summarytools::view(dfSummary(new.d$countyatdx, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 countyatdx [labelled, factor] addr at dx--state 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
2(0.1%)
1(0.0%)
8(0.2%)
3(0.1%)
3(0.1%)
1(0.0%)
6(0.2%)
15(0.4%)
3(0.1%)
6(0.2%)
1(0.0%)
3(0.1%)
34(1.0%)
1(0.0%)
4(0.1%)
1(0.0%)
2(0.1%)
5(0.1%)
5(0.1%)
1(0.0%)
32(0.9%)
1(0.0%)
8(0.2%)
4(0.1%)
3(0.1%)
2(0.1%)
11(0.3%)
4(0.1%)
4(0.1%)
1(0.0%)
61(1.7%)
11(0.3%)
2(0.1%)
4(0.1%)
6(0.2%)
4(0.1%)
1(0.0%)
25(0.7%)
2(0.1%)
1(0.0%)
9(0.3%)
12(0.3%)
19(0.5%)
95(2.7%)
8(0.2%)
34(1.0%)
14(0.4%)
10(0.3%)
9(0.3%)
3(0.1%)
13(0.4%)
41(1.2%)
7(0.2%)
8(0.2%)
8(0.2%)
228(6.4%)
1(0.0%)
77(2.2%)
17(0.5%)
3(0.1%)
55(1.5%)
7(0.2%)
5(0.1%)
119(3.3%)
1(0.0%)
14(0.4%)
6(0.2%)
2(0.1%)
7(0.2%)
1(0.0%)
10(0.3%)
55(1.5%)
21(0.6%)
3(0.1%)
2(0.1%)
2(0.1%)
265(7.5%)
1(0.0%)
2(0.1%)
6(0.2%)
19(0.5%)
7(0.2%)
5(0.1%)
5(0.1%)
12(0.3%)
1(0.0%)
1(0.0%)
18(0.5%)
7(0.2%)
20(0.6%)
2(0.1%)
5(0.1%)
4(0.1%)
8(0.2%)
2(0.1%)
4(0.1%)
1(0.0%)
39(1.1%)
1(0.0%)
1(0.0%)
1903(53.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
93(44.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
46(21.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
71(33.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
11(3.5%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
10(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
290(92.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
17(4.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
70(19.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
253(71.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
15(4.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
2(0.3%)
1(0.2%)
8(1.4%)
3(0.5%)
3(0.5%)
1(0.2%)
6(1.0%)
15(2.6%)
3(0.5%)
1(0.2%)
1(0.2%)
3(0.5%)
34(5.8%)
0(0.0%)
3(0.5%)
1(0.2%)
2(0.3%)
4(0.7%)
5(0.9%)
0(0.0%)
25(4.3%)
1(0.2%)
8(1.4%)
3(0.5%)
0(0.0%)
2(0.3%)
2(0.3%)
4(0.7%)
2(0.3%)
1(0.2%)
61(10.4%)
11(1.9%)
2(0.3%)
2(0.3%)
6(1.0%)
4(0.7%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
7(1.2%)
7(1.2%)
2(0.3%)
1(0.2%)
8(1.4%)
29(5.0%)
12(2.1%)
2(0.3%)
8(1.4%)
2(0.3%)
2(0.3%)
2(0.3%)
1(0.2%)
5(0.9%)
8(1.4%)
4(0.7%)
1(0.2%)
2(0.3%)
4(0.7%)
0(0.0%)
2(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
16(2.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
9(1.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
210(35.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
1(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
7(0.4%)
0(0.0%)
0(0.0%)
1(0.1%)
3(0.2%)
0(0.0%)
9(0.5%)
0(0.0%)
2(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
23(1.3%)
1(0.1%)
0(0.0%)
2(0.1%)
5(0.3%)
0(0.0%)
1(0.1%)
0(0.0%)
4(0.2%)
2(0.1%)
6(0.3%)
1(0.1%)
1(0.1%)
0(0.0%)
38(2.2%)
6(0.3%)
3(0.2%)
0(0.0%)
224(12.8%)
0(0.0%)
0(0.0%)
13(0.7%)
3(0.2%)
7(0.4%)
7(0.4%)
5(0.3%)
119(6.8%)
1(0.1%)
14(0.8%)
6(0.3%)
2(0.1%)
7(0.4%)
1(0.1%)
4(0.2%)
55(3.1%)
21(1.2%)
3(0.2%)
2(0.1%)
2(0.1%)
4(0.2%)
1(0.1%)
2(0.1%)
6(0.3%)
3(0.2%)
7(0.4%)
5(0.3%)
5(0.3%)
12(0.7%)
1(0.1%)
1(0.1%)
18(1.0%)
7(0.4%)
1(0.1%)
2(0.1%)
5(0.3%)
4(0.2%)
8(0.5%)
2(0.1%)
4(0.2%)
1(0.1%)
39(2.2%)
1(0.1%)
1(0.1%)
993(56.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
8(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COUNTY AT DX GEOCODE2000

  • Description: Code for the county of the patient’s residence at the time the tumor was diagnosed is a derived (geocoded) variable based on Census Boundary files from 2000 Decennial Census. This code should be used for county and county-based (such as CHSDA) rates and analysis for all cases diagnosed in 2000-2009.

  • Rationale: Census tracts are areas geographically nested within counties and designated with a 6-digit number code. This 6-digit code is commonly repeated within a state in different counties. Census tract numbers are only unique when paired with the state and the county. Therefore, a tract cannot be accurately identified without knowing the county. Example from Massachusetts: Rural Franklin County contains a tract 040600 with 2010 population 4,612 people. Urban Suffolk County contains a tract 040600 with 2,444 people. The county must be known in order to distinguish between the two tract codes. Because we historically used a single variable for county at diagnosis [90], correct tract codes were frequently paired with the wrong county due to incorrect county assignment during abstracting or a change of county over time. Also, some variables, such as the Census Tr Poverty Indicatr [145] require the use of the decennial Census County codes closest to year of diagnosis and not the decade of year of diagnosis. Using a single county at diagnosis, and using the reported versus geocoded data, may result in erroneous assignment of geographic location as well as invalid links with census data (i.e., population, poverty category, urban/rural designation).

  • Instructions for Coding

    • This variable is generated through the process of geocoding either during abstracting or at the central registry level.
    • It is recommended that all cases diagnosed through 2009 have a geocoded County at Diagnosis 2000.
    • At a minimum, all cases diagnosed through 1996-2009 should have a geocoded County at Diagnosis 2000. Cases diagnosed 1996-1999 must have both County at Diagnosis 1990 and County at Diagnosis 2000 codes for proper assignment of the Census Tract Poverty Indicator [145]. Cases diagnosed 2006-2009 must have both County at Diagnosis 2000 and County at Diagnosis 2010 codes for proper assignment of the Census Tract Poverty Indicator [145].
    • If the patient has multiple tumors, geocoded county may be different for each tumor.
    • Do not update this item if the patient’s county of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a county during manual geocoding or a consolidation process.
    • PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
    • Detailed standards have not been set for Canadian provinces/territories. Use code 998 for Canadian residents.
    • Blank “Not geocoded” is allowable for cases diagnosed before 1995 and after 2009. However, it is recommended to have all cases geocoded to a 2000 Census County to allow for both retrospective and cross-sectional analyses.

  • Codes

    • 001-997 County at diagnosis. Valid FIPS code.
    • 998 Known town, city, state, or country of residence but county code not known AND a resident outside of the state of reporting institution (must meet all criteria). Use this code for Canadian residents.
    • 999 County unknown. The county of the patient is unknown, or the patient is not a United States resident. County is not documented in the patient’s medical record.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#95


All data

st_css() #IMPORTANT!
  countyatdxgeocode2000 <- as.factor(trimws(d[,"countyatdxgeocode2000"]))
  
  new.d <- data.frame(new.d, countyatdxgeocode2000)
  new.d <- apply_labels(new.d, countyatdxgeocode2000 = "county at dx geocode 2000")
  new.d.1 <- data.frame(new.d.1, countyatdxgeocode2000)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, countyatdxgeocode2000)
  summarytools::view(dfSummary(new.d$countyatdxgeocode2000, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 countyatdxgeocode2000 [labelled, factor] county at dx geocode 2000 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99
1(0.3%)
2(0.6%)
11(3.2%)
1(0.3%)
4(1.2%)
21(6.2%)
10(2.9%)
2(0.6%)
13(3.8%)
1(0.3%)
19(5.6%)
2(0.6%)
42(12.3%)
51(15.0%)
62(18.2%)
49(14.4%)
14(4.1%)
2(0.6%)
22(6.5%)
4(1.2%)
8(2.3%)
 3216 (90.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99
1(0.3%)
2(0.6%)
11(3.5%)
1(0.3%)
0(0.0%)
0(0.0%)
10(3.2%)
2(0.6%)
13(4.1%)
1(0.3%)
19(6.0%)
2(0.6%)
42(13.3%)
51(16.2%)
62(19.7%)
49(15.6%)
14(4.4%)
2(0.6%)
22(7.0%)
4(1.3%)
7(2.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(10.5%)
17(89.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 337 (94.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_dx_geocode_2000 [factor] 1. 103 2. 107 3. 111 4. 113 5. 125 6. 163 7. 19 8. 25 9. 29 10. 39 11. 59 12. 61 13. 65 14. 67 15. 71 16. 73 17. 77 18. 83 19. 95 20. 97 21. 99
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(28.6%)
4(57.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
 9 (56.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COUNTY AT DX GEOCODE2010

  • Description: County at Diagnosis 2010 Code for the county of the patient’s residence at the time the tumor was diagnosed is a derived (geocoded) variable based on Census Boundary files from 2010 Decennial Census. This code should be used for county and county-based (such as CHSDA) rates and analysis for all cases diagnosed in 2010-2019.

  • Rationale: Census tracts are areas geographically nested within counties and designated with a 6-digit number code. This 6-digit code is commonly repeated within a state in different counties. Census tract numbers are only unique when paired with the state and the county. Therefore, a tract cannot be accurately identified without knowing the county. Example from Massachusetts: Rural Franklin County contains a tract 040600 with 2010 population 4,612 people. Urban Suffolk County contains a tract 040600 with 2,444 people. The county must be known in order to distinguish between the two tract codes. Because we historically used a single variable for county at diagnosis [90], correct tract codes were frequently paired with the wrong county due to incorrect county assignment during abstracting or a change of county over time. Also, some variables, such as the Census Tr Poverty Indicatr [145] require the use of the decennial Census County codes closest to year of diagnosis and not the decade of year of diagnosis. Using a single county at diagnosis, and using the reported versus geocoded data, may result in erroneous assignment of geographic location as well as invalid links with census data (i.e., population, poverty category, urban/rural designation).

  • Instructions for Coding

    • This variable is generated through the process of geocoding either during abstracting or at the central registry level.
    • It is recommended that all cases diagnosed through 2019 should have a geocoded County at Diagnosis 2010.
    • At a minimum, all cases diagnosed through 2006-2019 should have a geocoded County at Diagnosis 2010. Cases diagnosed 2006-2009 must have both County at Diagnosis 2000 and County at Diagnosis 2010 codes for proper assignment of the Census Tract Poverty Indicator [145]. Cases diagnosed 2016-2019 must have both County at Diagnosis 2010 and County at Diagnosis 2020 codes for proper assignment of the Census Tract Poverty Indicator [145].
    • If the patient has multiple tumors, geocoded county may be different for each tumor.
    • Do not update this item if the patient’s county of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a county during manual geocoding or a consolidation process.
    • PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
    • Detailed standards have not been set for Canadian provinces/territories. Use code 998 for Canadian residents.
    • Blank “Not geocoded” is allowable for cases diagnosed before 2005 and after 2019. However, it is preferred to have all cases geocoded to a 2010 Census County to allow for both retrospective and cross-sectional analyses.

  • Codes

    • 001-997 County at diagnosis. Valid FIPS code.
    • 998 Known town, city, state, or country of residence but county code not known AND a resident outside of the state of reporting institution (must meet all criteria). Use this code for Canadian residents.
    • 999 County unknown. The county of the patient is unknown, or the patient is not a United States resident. County is not documented in the patient’s medical record.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#96


All data

st_css() #IMPORTANT!
  countyatdxgeocode2010 <- as.factor(trimws(d[,"countyatdxgeocode2010"]))
  
  new.d <- data.frame(new.d, countyatdxgeocode2010)
  new.d <- apply_labels(new.d, countyatdxgeocode2010 = "county_at_dx_geocode_2010")
  new.d.1 <- data.frame(new.d.1, countyatdxgeocode2010)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, countyatdxgeocode2010)
  summarytools::view(dfSummary(new.d$countyatdxgeocode2010, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 countyatdxgeocode2010 [labelled, factor] county_at_dx_geocode_2010 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
2(0.1%)
1(0.0%)
8(0.2%)
3(0.1%)
3(0.1%)
1(0.0%)
6(0.2%)
15(0.4%)
3(0.1%)
6(0.2%)
1(0.0%)
3(0.1%)
34(1.0%)
1(0.0%)
4(0.1%)
1(0.0%)
2(0.1%)
5(0.1%)
5(0.1%)
1(0.0%)
32(0.9%)
1(0.0%)
8(0.2%)
4(0.1%)
3(0.1%)
2(0.1%)
11(0.3%)
4(0.1%)
4(0.1%)
1(0.0%)
61(1.7%)
11(0.3%)
2(0.1%)
4(0.1%)
6(0.2%)
4(0.1%)
1(0.0%)
25(0.7%)
2(0.1%)
1(0.0%)
9(0.3%)
12(0.3%)
19(0.5%)
95(2.7%)
8(0.2%)
34(1.0%)
14(0.4%)
10(0.3%)
9(0.3%)
3(0.1%)
13(0.4%)
41(1.2%)
7(0.2%)
8(0.2%)
8(0.2%)
228(6.4%)
1(0.0%)
77(2.2%)
17(0.5%)
3(0.1%)
55(1.5%)
7(0.2%)
5(0.1%)
119(3.3%)
1(0.0%)
14(0.4%)
6(0.2%)
2(0.1%)
7(0.2%)
1(0.0%)
10(0.3%)
55(1.5%)
21(0.6%)
3(0.1%)
2(0.1%)
2(0.1%)
265(7.5%)
1(0.0%)
2(0.1%)
6(0.2%)
19(0.5%)
7(0.2%)
5(0.1%)
5(0.1%)
12(0.3%)
1(0.0%)
1(0.0%)
18(0.5%)
7(0.2%)
20(0.6%)
2(0.1%)
5(0.1%)
4(0.1%)
8(0.2%)
2(0.1%)
4(0.1%)
1(0.0%)
39(1.1%)
1(0.0%)
1(0.0%)
1902(53.5%)
 1 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
320(100.0%)
 1 (0.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
93(44.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
46(21.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
71(33.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
11(3.5%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
10(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
290(92.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
17(4.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
70(19.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
253(71.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
15(4.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
2(0.3%)
1(0.2%)
8(1.4%)
3(0.5%)
3(0.5%)
1(0.2%)
6(1.0%)
15(2.6%)
3(0.5%)
1(0.2%)
1(0.2%)
3(0.5%)
34(5.8%)
0(0.0%)
3(0.5%)
1(0.2%)
2(0.3%)
4(0.7%)
5(0.9%)
0(0.0%)
25(4.3%)
1(0.2%)
8(1.4%)
3(0.5%)
0(0.0%)
2(0.3%)
2(0.3%)
4(0.7%)
2(0.3%)
1(0.2%)
61(10.4%)
11(1.9%)
2(0.3%)
2(0.3%)
6(1.0%)
4(0.7%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
7(1.2%)
7(1.2%)
2(0.3%)
1(0.2%)
8(1.4%)
29(5.0%)
12(2.1%)
2(0.3%)
8(1.4%)
2(0.3%)
2(0.3%)
2(0.3%)
1(0.2%)
5(0.9%)
8(1.4%)
4(0.7%)
1(0.2%)
2(0.3%)
4(0.7%)
0(0.0%)
2(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
16(2.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
9(1.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
210(35.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
1(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
7(0.4%)
0(0.0%)
0(0.0%)
1(0.1%)
3(0.2%)
0(0.0%)
9(0.5%)
0(0.0%)
2(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
23(1.3%)
1(0.1%)
0(0.0%)
2(0.1%)
5(0.3%)
0(0.0%)
1(0.1%)
0(0.0%)
4(0.2%)
2(0.1%)
6(0.3%)
1(0.1%)
1(0.1%)
0(0.0%)
38(2.2%)
6(0.3%)
3(0.2%)
0(0.0%)
224(12.8%)
0(0.0%)
0(0.0%)
13(0.7%)
3(0.2%)
7(0.4%)
7(0.4%)
5(0.3%)
119(6.8%)
1(0.1%)
14(0.8%)
6(0.3%)
2(0.1%)
7(0.4%)
1(0.1%)
4(0.2%)
55(3.1%)
21(1.2%)
3(0.2%)
2(0.1%)
2(0.1%)
4(0.2%)
1(0.1%)
2(0.1%)
6(0.3%)
3(0.2%)
7(0.4%)
5(0.3%)
5(0.3%)
12(0.7%)
1(0.1%)
1(0.1%)
18(1.0%)
7(0.4%)
1(0.1%)
2(0.1%)
5(0.3%)
4(0.2%)
8(0.5%)
2(0.1%)
4(0.2%)
1(0.1%)
39(2.2%)
1(0.1%)
1(0.1%)
993(56.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 county_at_d_geocode_2010 [factor] 1. 001 2. 003 3. 005 4. 007 5. 009 6. 013 7. 015 8. 017 9. 019 10. 021 11. 029 12. 031 13. 033 14. 035 15. 037 16. 039 17. 041 18. 045 19. 047 20. 049 21. 051 22. 053 23. 055 24. 057 25. 059 26. 061 27. 063 28. 065 29. 067 30. 069 31. 071 32. 073 33. 075 34. 077 35. 079 36. 083 37. 085 38. 089 39. 091 40. 093 41. 095 42. 097 43. 099 44. 1 45. 101 46. 103 47. 105 48. 107 49. 109 50. 11 51. 111 52. 113 53. 115 54. 117 55. 119 56. 121 57. 123 58. 125 59. 127 60. 129 61. 13 62. 131 63. 133 64. 135 65. 137 66. 139 67. 141 68. 143 69. 145 70. 149 71. 15 72. 151 73. 153 74. 155 75. 157 76. 161 77. 163 78. 165 79. 167 80. 169 81. 17 82. 171 83. 175 84. 177 85. 179 86. 181 87. 183 88. 185 89. 189 90. 19 91. 191 92. 193 93. 195 94. 199 95. 201 96. 207 97. 209 98. 21 99. 211 100. 213 [ 80 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
8(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CENSUS TRACT 2000

  • Description: Identifies the patient’s census tract of residence at the time the tumor was diagnosed. Census Tract 2000 is a derived (geocoded) variables based on the Census Boundary files from 2000. See Census Tract 70/80/90 [110]; Census Tract 2010 [135]; Census Tract 2020 [125]. Codes are those used by the U.S. Census Bureau for the Year 2000 Census. For consolidated records, the geocoded state should be based on the best address at diagnosis information identified.

  • Rationale: Census tract codes allow central registries to calculate incidence rates for geographical areas having population estimates. This field allows a central registry to add Year 2020 Census tracts to tumors diagnosed in previous years, without losing the codes in data items [110], [130] and [135].

  • The Census Bureau provides population and other demographic data for census tracts. This allows for small area analysis for general surveillance or special geographical and socioeconomic analysis.

  • Instructions for Coding

    • This variable is generated through the process of geocoding either during abstracting or at the central registry level.
    • Census tract codes have a 4-digit basic number and also may have a 2-digit suffix. Census tract numbers range from 0001.00 to 9999.98, but the decimal should not be retained in the NAACCR layout.
    • It is recommended that all cases diagnosed through 2009 should have a geocoded Census Tract 2000.
    • At a minimum, all cases diagnosed through 1996-2009 should have a geocoded Census Tract 2000. Cases diagnosed 1996-1999 must have both State at DX Geocode 70/80/90 [82] and State at DX 2000 Geocode [83] codes for proper assignment of the Census Tr Poverty Indicatr [145]. Cases diagnosed 2006-2009 must have both State at DX 2000 Geocode [83] and State at DX Geocode 2010 [84] codes for proper assignment of the Census Tr Poverty Indicatr [145].
    • If the patient has multiple tumors, geocoded state at diagnosis may be different for each tumor.
    • Do not update this item if the patient’s tract of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a tract during manual geocoding or a consolidation process.
    • PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
    • Blank “Not geocoded” is allowable for cases diagnosed before 1995 and after 2009. However, it is recommended to have all cases geocoded to a 2000 Census Tract to allow for both retrospective and cross-sectional analyses.

  • Codes

    • 000100-999998 Valid FIPS code
    • 000000 Area not census tracted
    • 999999 Area census-tracted, but census tract is not available
    • Blank Census Tract 2000 not coded

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#130


All data

st_css() #IMPORTANT!
  censustract2000 <- as.factor(trimws(d[,"censustract2000"]))
  
  levels(censustract2000)[levels(censustract2000)=="999999"] <- "not_available.999999"

  new.d <- data.frame(new.d, censustract2000)
  new.d <- apply_labels(new.d, censustract2000 = "census_tract_2000")
  new.d.1 <- data.frame(new.d.1, censustract2000)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, censustract2000)
  summarytools::view(dfSummary(new.d$censustract2000, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 censustract2000 [labelled, factor] census_tract_2000 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
2(0.2%)
1(0.1%)
1(0.1%)
845(96.9%)
 2685 (75.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
314(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999
1(5.3%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
0(0.0%)
0(0.0%)
1(5.3%)
0(0.0%)
2(10.5%)
1(5.3%)
0(0.0%)
0(0.0%)
 337 (94.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2000 [factor] 1. 136800 2. 160700 3. 160800 4. 166700 5. 255200 6. 30501 7. 503400 8. 504800 9. 505300 10. 512600 11. 513200 12. 513400 13. 514900 14. 516500 15. 517400 16. 520700 17. 521400 18. 521900 19. 522300 20. 524800 21. 538900 22. 540900 23. 541000 24. 542800 25. 545900 26. 561900 27. not_available.999999
0(0.0%)
0(0.0%)
1(14.3%)
1(14.3%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
1(14.3%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
 9 (56.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CENSUS TRACT 2010

  • Description: Identifies the patient’s census tract of residence at the time the tumor was diagnosed. Census Tract 2010 is a derived (geocoded) variables based on the Census Boundary files from 2010. See Census Tract 1970/80/90 [110]; Census Tract 2000 [130]; Census Tract 2020 [125]. Codes are those used by the U.S. Census Bureau for the Year 2010 Census. For consolidated records, the geocoded state should be based on the best address at diagnosis information identified.

  • Rationale: Census tract codes allow central registries to calculate incidence rates for geographical areas having population estimates. This field allows a central registry to add Year 2020 Census tracts to tumors diagnosed in previous years, without losing the codes in data items [110], [130] and [135].

  • The Census Bureau provides population and other demographic data for census tracts. This allows for small area analysis for general surveillance or special geographical and socioeconomic analysis.

  • Instructions for Coding

    • This variable is generated through the process of geocoding either during abstracting or at the central registry level.
    • Census tract codes have a 4-digit basic number and also may have a 2-digit suffix. Census tract numbers range from 0001.00 to 9999.98, but the decimal should not be retained in the NAACCR layout.
    • It is recommended that all cases diagnosed through 2019 should have a geocoded Census Tract 2010.
    • At a minimum, all cases diagnosed through 2006-2019 should have a geocoded Census Tract 2010. Cases diagnosed 2006-2009 must have both State at DX Geocode 2000 [82] and State at DX Geocode 2010 [83] codes for proper assignment of the Census Tr Poverty Indicatr [145]. Cases diagnosed 2016-2019 must have both State at DX Geocode 2010 [83] and State at DX Geocode 2020 [84] codes for proper assignment of the Census Tr Poverty Indicatr [145].
    • If the patient has multiple tumors, geocoded state at diagnosis may be different for each tumor.
    • Do not update this item if the patient’s tract of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a tract during manual geocoding or a consolidation process.
    • PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
    • Blank “Not geocoded” is allowable for cases diagnosed before 2005 and after 2019. However, it is preferred to have all cases geocoded to a 2010 Census Tract to allow for both retrospective and cross-sectional analyses.

  • Codes

    • 000100-999998 Valid FIPS code
    • 000000 Area not census tracted
    • 999999 Area census tracted, but census tract is not available
    • Blank Census Tract 2010 not coded

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#135


All data

st_css() #IMPORTANT!
  censustract2010 <- as.factor(trimws(d[,"censustract2010"]))
  
  levels(censustract2010)[levels(censustract2010)=="999999"] <- "not_available.999999"

  new.d <- data.frame(new.d, censustract2010)
  new.d <- apply_labels(new.d, censustract2010 = "census_tract_2010")
  new.d.1 <- data.frame(new.d.1, censustract2010)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, censustract2010)
  summarytools::view(dfSummary(new.d$censustract2010, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 censustract2010 [labelled, factor] census_tract_2010 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
3(0.1%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
3(0.1%)
2(0.1%)
2(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
3(0.1%)
2(0.1%)
3(0.1%)
4(0.1%)
2(0.1%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
3(0.1%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
3(0.1%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
2(0.1%)
1(0.0%)
3(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
1(0.0%)
2(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
3415(96.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
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0(0.0%)
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0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
0(0.0%)
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0(0.0%)
0(0.0%)
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
356(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
3(0.5%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
3(0.5%)
2(0.3%)
2(0.3%)
2(0.3%)
0(0.0%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
0(0.0%)
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0(0.0%)
1(0.2%)
1(0.2%)
2(0.3%)
3(0.5%)
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3(0.5%)
4(0.7%)
2(0.3%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
3(0.5%)
0(0.0%)
1(0.2%)
2(0.3%)
0(0.0%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
2(0.3%)
1(0.2%)
3(0.5%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
0(0.0%)
1(0.2%)
2(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
1(0.2%)
1(0.2%)
0(0.0%)
2(0.3%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
467(79.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
0(0.0%)
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1(0.1%)
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1(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
0(0.0%)
0(0.0%)
2(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
1(0.1%)
2(0.1%)
2(0.1%)
1(0.1%)
1(0.1%)
0(0.0%)
1730(98.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tract_2010 [factor] 1. 000100 2. 000102 3. 000200 4. 000300 5. 000600 6. 000602 7. 000603 8. 000604 9. 000605 10. 000606 11. 000611 12. 000613 13. 000616 14. 000617 15. 000618 16. 000700 17. 000702 18. 000900 19. 001000 20. 001004 21. 001100 22. 001104 23. 001200 24. 001300 25. 001302 26. 001409 27. 001601 28. 001606 29. 001607 30. 001701 31. 001702 32. 001720 33. 001723 34. 001724 35. 001725 36. 001740 37. 001745 38. 001747 39. 001748 40. 001751 41. 001800 42. 002001 43. 002002 44. 002100 45. 002104 46. 002200 47. 002300 48. 002501 49. 002503 50. 002504 51. 002600 52. 003000 53. 003100 54. 003103 55. 003201 56. 003202 57. 003300 58. 003301 59. 003308 60. 003400 61. 003502 62. 003504 63. 003506 64. 003507 65. 003801 66. 003805 67. 003907 68. 003908 69. 004000 70. 004005 71. 004014 72. 004201 73. 004203 74. 004401 75. 004509 76. 004604 77. 005100 78. 005300 79. 005302 80. 005500 81. 006100 82. 006300 83. 007101 84. 007706 85. 007802 86. 008000 87. 008102 88. 008500 89. 008800 90. 009200 91. 010102 92. 010201 93. 010300 94. 010301 95. 010302 96. 010303 97. 010304 98. 010500 99. 010501 100. 010503 [ 1915 others ]
0(0.0%)
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0(0.0%)
0(0.0%)
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0(0.0%)
0(0.0%)
0(0.0%)
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


MARITAL STATUS AT DX

  • Description: Code for the patient’s marital status at the time of diagnosis for the reportable tumor. If the patient has multiple tumors, marital status may be different for each tumor.
  • Rationale: Incidence and survival with certain cancers vary by marital status. The item also helps in patient identification.
  • Codes
    • 1 Single (never married)
    • 2 Married (including common law)
    • 3 Separated
    • 4 Divorced
    • 5 Widowed
    • 6 Unmarried or Domestic Partner (same sex or opposite sex, registered or unregistered, other than common law marriage)
    • 9 Unknown
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#150


All data

st_css() #IMPORTANT!
  maritalstatusatdx <- as.factor(trimws(d[,"maritalstatusatdx"]))
  
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="1"] <- "Single.1"
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="2"] <- "Married.2"
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="3"] <- "Separated.3"
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="4"] <- "Divorced.4"
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="5"] <- "Widowed.5"
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="6"] <- "Unmarried_or_Domestic_Partner.6"
  levels(maritalstatusatdx)[levels(maritalstatusatdx)=="9"] <- "Unknown.9"


  new.d <- data.frame(new.d, maritalstatusatdx)
  new.d <- apply_labels(new.d, maritalstatusatdx = "marital_status_at_dx")
  new.d.1 <- data.frame(new.d.1, maritalstatusatdx)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, maritalstatusatdx)
  summarytools::view(dfSummary(new.d$maritalstatusatdx, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 maritalstatusatdx [labelled, factor] marital_status_at_dx 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
649(18.2%)
2003(56.3%)
59(1.7%)
280(7.9%)
92(2.6%)
14(0.4%)
460(12.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
92(28.7%)
164(51.1%)
6(1.9%)
29(9.0%)
6(1.9%)
3(0.9%)
21(6.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
50(23.8%)
112(53.3%)
1(0.5%)
12(5.7%)
3(1.4%)
1(0.5%)
31(14.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
53(16.8%)
200(63.5%)
2(0.6%)
19(6.0%)
4(1.3%)
5(1.6%)
32(10.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
101(28.4%)
167(46.9%)
10(2.8%)
37(10.4%)
8(2.2%)
0(0.0%)
33(9.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
114(19.5%)
328(56.1%)
12(2.1%)
55(9.4%)
22(3.8%)
0(0.0%)
54(9.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
236(13.5%)
1023(58.3%)
28(1.6%)
126(7.2%)
47(2.7%)
5(0.3%)
289(16.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 marital_status_at_dx [factor] 1. Single.1 2. Married.2 3. Separated.3 4. Divorced.4 5. Widowed.5 6. Unmarried_or_Domestic_Par 7. Unknown.9
3(18.8%)
9(56.2%)
0(0.0%)
2(12.5%)
2(12.5%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RACE 1

  • Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.

  • Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).

  • Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.

  • Codes

    • 01 White
    • 02 Black

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#160


All data

st_css() #IMPORTANT!
  race1 <- as.factor(trimws(d[,"race1"]))
  
  levels(race1)[levels(race1)=="1"] <- "White.1"
  levels(race1)[levels(race1)=="2"] <- "Black.2"
  
  new.d <- data.frame(new.d, race1)
  new.d <- apply_labels(new.d, race1 = "race_1")
  new.d.1 <- data.frame(new.d.1, race1)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, race1)
  summarytools::view(dfSummary(new.d$race1, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [labelled, factor] race_1 1. 02 2. White.1 3. Black.2
621(17.5%)
1(0.0%)
2935(82.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
182(51.1%)
0(0.0%)
174(48.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
287(49.1%)
0(0.0%)
298(50.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
152(8.7%)
1(0.1%)
1601(91.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race1 [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RACE 2

-Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.

-Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G). -Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories. - Codes + 01 White + 02 Black + 88 No further race documented


All data

st_css() #IMPORTANT!
  race2 <- as.factor(trimws(d[,"race2"]))
  
  levels(race2)[levels(race2)=="1"] <- "White.1"
  levels(race2)[levels(race2)=="88"] <- "No_further_race.88"
  
  new.d <- data.frame(new.d, race2)
  new.d <- apply_labels(new.d, race2 = "race_2")
  new.d.1 <- data.frame(new.d.1, race2)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, race2)
  summarytools::view(dfSummary(new.d$race2, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [labelled, factor] race_2 1. 01 2. White.1 3. No_further_race.88
1(0.0%)
16(0.4%)
3540(99.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
0(0.0%)
3(0.9%)
318(99.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
0(0.0%)
8(3.8%)
202(96.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
0(0.0%)
5(1.6%)
310(98.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
1(0.3%)
0(0.0%)
355(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
0(0.0%)
0(0.0%)
585(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
0(0.0%)
0(0.0%)
1754(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race2 [factor] 1. 01 2. White.1 3. No_further_race.88
0(0.0%)
0(0.0%)
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RACE 3

  • Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.

  • Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).

  • Rationale： Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.

  • Codes

    • 01 White
    • 02 Black
    • 88 No further race documented

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#162


All data

st_css() #IMPORTANT!
  race3 <- as.factor(trimws(d[,"race3"]))
  
  levels(race3)[levels(race3)=="1"] <- "White.1"
  levels(race3)[levels(race3)=="88"] <- "No_further_race.88"
  
  new.d <- data.frame(new.d, race3)
  new.d <- apply_labels(new.d, race3 = "race_3")
  new.d.1 <- data.frame(new.d.1, race3)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, race3)
  summarytools::view(dfSummary(new.d$race3, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [labelled, factor] race_3 1. No_further_race.88
3557(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
356(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
585(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
1754(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race3 [factor] 1. No_further_race.88
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RACE 4

  • Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.

  • Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).

  • Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.

  • Codes

    • 01 White
    • 02 Black
    • 88 No further race documented

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#163


All data

st_css() #IMPORTANT!
  race4 <- as.factor(trimws(d[,"race4"]))
  
  levels(race4)[levels(race4)=="1"] <- "White.1"
  levels(race4)[levels(race4)=="88"] <- "No_further_race.88"
  
  new.d <- data.frame(new.d, race4)
  new.d <- apply_labels(new.d, race4 = "race_4")
  new.d.1 <- data.frame(new.d.1, race4)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, race4)
  summarytools::view(dfSummary(new.d$race4, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [labelled, factor] race_4 1. No_further_race.88
3557(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
356(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
585(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
1754(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race4 [factor] 1. No_further_race.88
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RACE 5

  • Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.

  • Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).

  • Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.

  • Codes

    • 01 White
    • 02 Black
    • 88 No further race documented

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#164


All data

st_css() #IMPORTANT!
  race5 <- as.factor(trimws(d[,"race5"]))
  
  levels(race5)[levels(race5)=="1"] <- "White.1"
  levels(race5)[levels(race5)=="88"] <- "No_further_race.88"
  
  new.d <- data.frame(new.d, race5)
  new.d <- apply_labels(new.d, race5 = "race_5")
  new.d.1 <- data.frame(new.d.1, race5)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, race5)
  summarytools::view(dfSummary(new.d$race5, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [labelled, factor] race_5 1. No_further_race.88
3557(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
356(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
585(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
1754(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race5 [factor] 1. No_further_race.88
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SPANISH/HISPANIC ORIGIN

  • Description: Code identifying persons of Spanish or Hispanic origin. This code is used by hospital and central registries to show the “best guess” as to whether or not the person should be classified as Hispanic for purposes of calculating cancer rates. If the patient has multiple tumors, all records should have the same code.
  • Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf. All information resources should be used to determine the correct code, including:
    • Stated ethnicity in the medical record
    • Stated Hispanic origin on the death certificate
    • Birthplace
    • Information about life history and/or language spoken found during the abstracting process
    • Patient’s last name [2230] or maiden name [2390] found on a list of Hispanic names
  • Some registries code the information from the medical record, others code ethnicity based on Spanish names, and others use a combination of methods.
  • Persons of Spanish or Hispanic origin may be of any race, but these categories generally are not used for Native Americans, Filipinos, etc., who may have Spanish names. If a patient has an Hispanic name, but there is reason to believe they are not Hispanic (e.g., the patient is Filipino, or the patient is a woman known to be non-Hispanic who has a Hispanic married name), the code in this field should be 0 (non-Spanish, non-Hispanic). The code in item Computed Ethnicity [200], however, would reflect the Hispanic name.
  • Assign code 7 if Hispanic ethnicity is based strictly on a computer list or algorithm (unless contrary evidence is available) and also code in Computed Ethnicity [200].
  • Rationale: See the rationales for the Race 1-5 [160-164] and Computed Ethnicity [200]. Ethnic origin has a significant association with cancer rates and outcomes. Hispanic populations have different patterns of occurrence of cancer from other populations that may be included in the “white” category of Race [160].
  • Codes
    • 0 Non-Spanish; non-Hispanic
    • 1 Mexican (includes Chicano)
    • 2 Puerto Rican
    • 3 Cuban
    • 4 South or Central American (except Brazil)
    • 5 Other specified Spanish/Hispanic origin (includes European; excludes Dominican Republic)
    • 6 Spanish, NOS Hispanic, NOS Latino, NOS There is evidence, other than surname or maiden name, that the person is Hispanic, but he/she cannot be assigned to any of the other categories 1-5.
    • 7 Spanish surname only (Code 7 is ordinarily for central registry use only, hospital registrars may use code 7 if using a list of Hispanic surnames provided by their central registry; otherwise, code 9 ‘unknown whether Spanish or not’ should be used.) The only evidence of the person’s Hispanic origin is the surname or maiden name and there is no contrary evidence that the person is not Hispanic.
    • 8 Dominican Republic
    • 9 Unknown whether Spanish or not
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#190


All data

st_css() #IMPORTANT!
  spanishhispanicorigin <- as.factor(trimws(d[,"spanishhispanicorigin"]))
  
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="0"] <- "Non_Spanish_Hispanic.0"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="1"] <- "Mexican.1"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="2"] <- "Puerto_Rican.2"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="3"] <- "Cuban.3"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="4"] <- "South_or_Central_American.4)"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="5"] <- "Other_specified.5"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="6"] <- "Spanish_Hispanic_Latino_NOS.6"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="7"] <- "Spanish_surname_only.7"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="8"] <- "Dominican_Republic.8"
  levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="9"] <- "Unknown.9"

  new.d <- data.frame(new.d, spanishhispanicorigin)
  new.d <- apply_labels(new.d, spanishhispanicorigin = "spanish_hispanic_origin")
  new.d.1 <- data.frame(new.d.1, spanishhispanicorigin)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, spanishhispanicorigin)
  summarytools::view(dfSummary(new.d$spanishhispanicorigin, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 spanishhispanicorigin [labelled, factor] spanish_hispanic_origin 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
3513(98.8%)
3(0.1%)
2(0.1%)
1(0.0%)
2(0.1%)
9(0.3%)
2(0.1%)
25(0.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
318(99.1%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
208(99.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
312(99.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
354(99.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
569(97.3%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
14(2.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
1736(99.0%)
2(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
3(0.2%)
1(0.1%)
11(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 spanish_hispanic_origin [factor] 1. Non_Spanish_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. Dominican_Republic.8 8. Unknown.9
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


NHIA DERIVED HISP ORIGIN

  • Description: The NAACCR Hispanic Identification Algorithm (NHIA) uses a combination of standard variables to directly or indirectly classify cases as Hispanic for analytic purposes. It is possible to separate Hispanic ancestral subgroups (e.g., Mexican) when indirect assignment results from birthplace information but not from surname match. The algorithm uses the following standard variables: Spanish/Hispanic Origin [190], Name–Last [2230], Name–Maiden [2390], Birthplace [250], Race 1 [160], IHS Link [192], and Sex [220].

  • Code 7 (Spanish surname only) of the Spanish/Hispanic Origin [190] data item became effective with 1994 diagnoses. It is recommended that NHIA should be run on 1995 and later diagnoses. However, a central registry may run it on their data for prior years. For greater detail, please refer to the technical documentation: http://www.naaccr.org/LinkClick.aspx?fileticket=6E20OT41TcA%3d&tabid=118&mid=458.

  • Rationale: Sometimes despite best efforts to obtain complete information directly from the medical record, information is not available and is reported to the cancer registry as a missing data item. With regard to Hispanic ethnicity, some cancer registries have found it necessary to rely on indirect methods to populate this data element. Registries often have significant numbers or proportions of Hispanic populations in their jurisdiction.

  • Codes

    • 0 Non-Hispanic
    • 1 Mexican, by birthplace or other specific identifier
    • 2 Puerto Rican, by birthplace or other specific identifier
    • 3 Cuban, by birthplace or other specific identifier
    • 4 South or Central American (except Brazil), by birthplace or other specific identifier
    • 5 Other specified Spanish/Hispanic origin (includes European; excludes Dominican Republic), by birthplace or other specific identifier
    • 6 Spanish, NOS; Hispanic, NOS; Latino, NOS
    • 7 NHIA surname match only
    • 8 Dominican Republic
    • Blank Algorithm has not been run

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#191


All data

st_css() #IMPORTANT!
  nhiaderivedhisporigin <- as.factor(trimws(d[,"nhiaderivedhisporigin"]))
  
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="0"] <- "Non_Hispanic.0"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="1"] <- "Mexican.1"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="2"] <- "Puerto_Rican.2"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="3"] <- "Cuban.3"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="4"] <- "South_or_Central_American.4)"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="5"] <- "Other_specified.5"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="6"] <- "Spanish_Hispanic_Latino_NOS.6"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="7"] <- "NHIA_surname_match.7"
  levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="8"] <- "Dominican_Republic.8"

  new.d <- data.frame(new.d, nhiaderivedhisporigin)
  new.d <- apply_labels(new.d, nhiaderivedhisporigin = "nhia_derived_hisp_origin")
  new.d.1 <- data.frame(new.d.1, nhiaderivedhisporigin)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, nhiaderivedhisporigin)
  summarytools::view(dfSummary(new.d$nhiaderivedhisporigin, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 nhiaderivedhisporigin [labelled, factor] nhia_derived_hisp_origin 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
3532(99.3%)
3(0.1%)
2(0.1%)
2(0.1%)
2(0.1%)
9(0.3%)
5(0.1%)
2(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
317(98.8%)
0(0.0%)
1(0.3%)
2(0.6%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
207(98.6%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
1(0.5%)
1(0.5%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
312(99.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
354(99.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
582(99.5%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
1(0.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
1744(99.4%)
2(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
3(0.2%)
3(0.2%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 nhia_derived_hisp_origin [factor] 1. Non_Hispanic.0 2. Mexican.1 3. Cuban.3 4. South_or_Central_American 5. Other_specified.5 6. Spanish_Hispanic_Latino_N 7. NHIA_surname_match.7 8. Dominican_Republic.8
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RACE–NAPIIA(DERIVED API)

  • Description: NAPIIA is an acronym for NAACCR Asian and Pacific Islander Identification Algorithm. Race–NAPIIA(derived API) recodes some single-race cases with a Race 1 [160] code of 96 to a more specific Asian race category, based on an algorithm that makes use of the birthplace and name fields (first, last, and maiden names). For single-race cases with a Race 1 code other than 96, it returns the Race 1 code. Multiple-race cases (those with information in Race 2 through Race 5, [161-164]) are handled variously; for greater detail please refer to the technical documentation: http://www.naaccr.org/LinkClick.aspx?fileticket=3HnBhlmhkBs%3d&tabid=118&mid=458

  • In Version 1.1 of the algorithm, birthplace can be used to indirectly assign a specific race to one of eight Asian race groups (Chinese, Japanese, Vietnamese, Korean, Asian Indian, Filipino, Thai, and Cambodian), and names can be used to indirectly assign a specific race to one of seven Asian groups (Chinese, Japanese, Vietnamese, Korean, Asian Indian, Filipino, and Hmong). Subsequent versions of NAPIIA may incorporate Pacific Islanders and may potentially incorporate name lists for Thai, Cambodian, and Laotians.

  • Rationale: The use of more specific Asian and Pacific Islander codes will enhance surveillance and research activities focused on specific API subgroups.

  • Codes

    • 01 White
    • 02 Black

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#193


All data

st_css() #IMPORTANT!
  racenapiia <- as.factor(trimws(d[,"racenapiia"]))
  
  levels(racenapiia)[levels(racenapiia)=="1"] <- "White.1"
  levels(racenapiia)[levels(racenapiia)=="2"] <- "Black.2"

  new.d <- data.frame(new.d, racenapiia)
  new.d <- apply_labels(new.d, racenapiia = "race_napiia")
  new.d.1 <- data.frame(new.d.1, racenapiia)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, racenapiia)
  summarytools::view(dfSummary(new.d$racenapiia, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 racenapiia [labelled, factor] race_napiia 1. 02 2. White.1 3. Black.2
621(17.5%)
1(0.0%)
2935(82.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
182(51.1%)
0(0.0%)
174(48.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
287(49.1%)
0(0.0%)
298(50.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
152(8.7%)
1(0.1%)
1601(91.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 race_napiia [factor] 1. 02 2. White.1 3. Black.2
0(0.0%)
0(0.0%)
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SEX


All data

st_css() #IMPORTANT!
  sex <- as.factor(trimws(d[,"sex"]))
  
  levels(sex)[levels(sex)=="1"] <- "Male.1"
  levels(sex)[levels(sex)=="2"] <- "Female.2"

  new.d <- data.frame(new.d, sex)
  new.d <- apply_labels(new.d, sex = "sex")
  new.d.1 <- data.frame(new.d.1, sex)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, sex)
  summarytools::view(dfSummary(new.d$sex, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 sex [labelled, factor] sex 1. Male.1 2. Female.2
3556(100.0%)
1(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
321(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
210(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
315(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
356(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
585(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
1753(99.9%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sex [factor] 1. Male.1 2. Female.2
16(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AGE AT DIAGNOSIS

  • Description: Age of the patient at diagnosis in complete years. Different tumors for the same patient may have different values.
  • Codes
    • 000 Less than 1 year old; diagnosed in utero
    • 001 1 year old, but less than 2 years
    • 002 2 years old …
    • 101 101 years old …
    • 120 120 years old
    • 999 Unknown age
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#230


All data

st_css() #IMPORTANT!
  ageatdiagnosis <- trimws(d[,"ageatdiagnosis"])
  ageatdiagnosis <- ifelse(ageatdiagnosis=="999", NA, ageatdiagnosis)
  ageatdiagnosis <- as.numeric(ageatdiagnosis)
  
  new.d <- data.frame(new.d, ageatdiagnosis)
  new.d <- apply_labels(new.d, ageatdiagnosis = "age_at_diagnosis")
  new.d.1 <- data.frame(new.d.1, ageatdiagnosis)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, ageatdiagnosis)
  summarytools::view(dfSummary(new.d$ageatdiagnosis, style = 'grid',
                               max.distinct.values = 10, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ageatdiagnosis [labelled, numeric] age_at_diagnosis Mean (sd) : 62 (6.9) min < med < max: 26 < 62 < 79 IQR (CV) : 10 (0.1) 42 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 62 (7.1) min < med < max: 43 < 62 < 79 IQR (CV) : 9 (0.1)
43:2(0.6%)
46:1(0.3%)
47:3(0.9%)
48:3(0.9%)
49:10(3.1%)
50:3(0.9%)
51:7(2.2%)
52:2(0.6%)
53:11(3.4%)
54:9(2.8%)
55:10(3.1%)
56:9(2.8%)
57:9(2.8%)
58:11(3.4%)
59:16(5.0%)
60:24(7.5%)
61:17(5.3%)
62:17(5.3%)
63:19(5.9%)
64:13(4.0%)
65:22(6.9%)
66:19(5.9%)
67:10(3.1%)
68:16(5.0%)
69:17(5.3%)
70:6(1.9%)
71:5(1.6%)
72:8(2.5%)
73:7(2.2%)
74:6(1.9%)
75:2(0.6%)
76:1(0.3%)
77:1(0.3%)
78:4(1.2%)
79:1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 62.3 (6.9) min < med < max: 40 < 63 < 79 IQR (CV) : 9 (0.1)
40:1(0.5%)
45:2(1.0%)
46:2(1.0%)
48:2(1.0%)
49:4(1.9%)
50:2(1.0%)
51:4(1.9%)
52:2(1.0%)
53:2(1.0%)
54:5(2.4%)
55:7(3.3%)
56:6(2.9%)
57:13(6.2%)
58:9(4.3%)
59:12(5.7%)
60:7(3.3%)
61:10(4.8%)
62:8(3.8%)
63:8(3.8%)
64:16(7.6%)
65:13(6.2%)
66:14(6.7%)
67:12(5.7%)
68:13(6.2%)
69:11(5.2%)
70:4(1.9%)
71:5(2.4%)
72:6(2.9%)
73:3(1.4%)
74:4(1.9%)
77:1(0.5%)
78:1(0.5%)
79:1(0.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 61.6 (6.6) min < med < max: 38 < 62 < 74 IQR (CV) : 9.5 (0.1)
38:1(0.3%)
42:1(0.3%)
44:1(0.3%)
47:3(1.0%)
48:1(0.3%)
49:1(0.3%)
50:5(1.6%)
51:7(2.2%)
52:11(3.5%)
53:7(2.2%)
54:7(2.2%)
55:17(5.4%)
56:12(3.8%)
57:13(4.1%)
58:9(2.9%)
59:23(7.3%)
60:19(6.0%)
61:12(3.8%)
62:18(5.7%)
63:14(4.4%)
64:12(3.8%)
65:17(5.4%)
66:25(7.9%)
67:15(4.8%)
68:11(3.5%)
69:15(4.8%)
70:13(4.1%)
71:10(3.2%)
72:4(1.3%)
73:3(1.0%)
74:8(2.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 61.8 (7.4) min < med < max: 38 < 61 < 79 IQR (CV) : 10 (0.1)
38:1(0.3%)
43:1(0.3%)
44:3(0.8%)
45:3(0.8%)
47:1(0.3%)
48:1(0.3%)
49:4(1.1%)
50:8(2.2%)
51:3(0.8%)
52:5(1.4%)
53:9(2.5%)
54:11(3.1%)
55:14(3.9%)
56:18(5.1%)
57:18(5.1%)
58:20(5.6%)
59:25(7.0%)
60:22(6.2%)
61:20(5.6%)
62:19(5.3%)
63:14(3.9%)
64:10(2.8%)
65:15(4.2%)
66:12(3.4%)
67:14(3.9%)
68:14(3.9%)
69:10(2.8%)
70:12(3.4%)
71:10(2.8%)
72:9(2.5%)
73:10(2.8%)
74:4(1.1%)
75:5(1.4%)
76:1(0.3%)
77:4(1.1%)
78:4(1.1%)
79:2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 62.8 (6.8) min < med < max: 44 < 63 < 79 IQR (CV) : 9 (0.1)
44:1(0.2%)
45:1(0.2%)
46:1(0.2%)
47:5(0.9%)
48:3(0.5%)
49:6(1.0%)
50:5(0.9%)
51:9(1.5%)
52:11(1.9%)
53:13(2.2%)
54:12(2.1%)
55:22(3.8%)
56:26(4.4%)
57:15(2.6%)
58:18(3.1%)
59:40(6.8%)
60:27(4.6%)
61:32(5.5%)
62:28(4.8%)
63:38(6.5%)
64:32(5.5%)
65:39(6.7%)
66:20(3.4%)
67:40(6.8%)
68:22(3.8%)
69:26(4.4%)
70:17(2.9%)
71:17(2.9%)
72:12(2.1%)
73:9(1.5%)
74:14(2.4%)
75:7(1.2%)
76:4(0.7%)
77:3(0.5%)
78:7(1.2%)
79:3(0.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 61.9 (6.7) min < med < max: 26 < 63 < 74 IQR (CV) : 10 (0.1)
26:1(0.1%)
40:1(0.1%)
41:4(0.2%)
42:4(0.2%)
43:3(0.2%)
44:5(0.3%)
45:4(0.2%)
46:8(0.5%)
47:16(0.9%)
48:15(0.9%)
49:12(0.7%)
50:31(1.8%)
51:31(1.8%)
52:27(1.5%)
53:35(2.0%)
54:52(3.0%)
55:64(3.6%)
56:58(3.3%)
57:72(4.1%)
58:79(4.5%)
59:92(5.2%)
60:86(4.9%)
61:86(4.9%)
62:82(4.7%)
63:92(5.2%)
64:94(5.4%)
65:114(6.5%)
66:119(6.8%)
67:94(5.4%)
68:79(4.5%)
69:77(4.4%)
70:56(3.2%)
71:44(2.5%)
72:40(2.3%)
73:46(2.6%)
74:31(1.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 age_at_diagnosis [numeric] Mean (sd) : 58 (5.3) min < med < max: 47 < 58 < 67 IQR (CV) : 7.5 (0.1)
47:1(6.2%)
52:1(6.2%)
53:1(6.2%)
54:1(6.2%)
55:1(6.2%)
56:2(12.5%)
58:2(12.5%)
59:2(12.5%)
62:1(6.2%)
63:1(6.2%)
64:1(6.2%)
65:1(6.2%)
67:1(6.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DATE OF BIRTH

  • Description: Date of birth of the patient. See Chapter X for date format. If age at diagnosis and year of diagnosis are known, but year of birth is unknown, then year of birth should be calculated and so coded. Only the year should be entered, left-justified. Estimate date of birth when information is not available. It is better to estimate than to leave birth date unknown.

  • date var

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#240


All data

st_css() #IMPORTANT!
  dateofbirth <- trimws(d[,"dateofbirth"])
  select99 <- ifelse(is.na(dateofbirth), F, substr(dateofbirth, start=7, stop=8)=="99")
  dateofbirth[select99] <- substr(dateofbirth[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(dateofbirth), F, nchar(trimws(dateofbirth))==6)
  dateofbirth[select6] <- paste(dateofbirth[select6], "15", sep="")
  
  select4 <- ifelse(is.na(dateofbirth), F, nchar(trimws(dateofbirth))==4)
  dateofbirth[select4] <- paste(dateofbirth[select4], "0615", sep="")
  
  dateofbirth <- as.Date(dateofbirth, c("%Y%m%d"))
  
  new.d <- data.frame(new.d, dateofbirth)
  new.d <- apply_labels(new.d, dateofbirth = "date_of_birth")
  #new.d.1 <- data.frame(new.d.1, dateofbirth)
  
  #Using kable function to form a nice table
  temp.d <- data.frame (new.d.1, dateofbirth)
  summarytools::view(dfSummary(new.d$dateofbirth, style = 'grid',
                               max.distinct.values = 5, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 dateofbirth [labelled, Date] date_of_birth min : 1933-05-15 med : 1953-05-02 max : 1990-08-29 range : 57y 3m 14d 2453 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] min : 1937-07-15 med : 1953-06-15 max : 1973-03-15 range : 35y 8m 0d 207 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] min : 1937-04-16 med : 1952-03-17 max : 1976-03-03 range : 38y 10m 16d 207 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] min : 1941-01-15 med : 1953-09-15 max : 1978-05-15 range : 37y 4m 0d 193 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] min : 1933-05-15 med : 1954-09-30 max : 1977-07-15 range : 44y 2m 0d 217 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] min : 1936-09-12 med : 1953-03-06 max : 1972-11-20 range : 36y 2m 8d 561 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] min : 1940-03-22 med : 1953-01-10 max : 1990-08-29 range : 50y 5m 7d 1586 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_birth [Date] 1. 1945-04-15 2. 1948-09-15 3. 1949-02-15 4. 1949-06-15 5. 1950-12-15 6. 1952-04-15 7. 1953-08-15 8. 1954-02-15 9. 1955-07-15 10. 1956-01-15 11. 1956-10-15 12. 1956-12-15 13. 1959-10-15 14. 1960-05-15 15. 1962-04-15 16. 1966-07-15
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


BIRTHPLACE


  • Description: Code for place of birth of the patient. If a patient has multiple tumors, all records should contain the same code.

  • Rationale: Place of Birth is helpful for patient matching and can be used when reviewing race and ethnicity. In addition, adding birthplace data to race and ethnicity allows for a more specific definition of the population being reported. Careful descriptions of ancestry, birthplace, and immigration history of populations studied are needed to make the basis for classification into ethnic groups clear. Birthplace has been associated with variation in genetic, socioeconomic, cultural, and nutritional characteristics that affect patterns of disease. A better understanding of the differences within racial and ethnic categories also can help states develop effective, culturally sensitive public health prevention programs to decrease the prevalence of high-risk behaviors and increase the use of preventive services.

  • Note: For cases diagnosed January 1, 2013, and later, Birthplace–State [252] and Birthplace–Country [254] replace Birthplace [250].

  • Codes: See Appendix B for numeric and alphabetic lists of places and codes (also see Appendix B of the SEER Program Code Manual at seer.cancer.gov/tools/codingmanuals/index.html).

  • Many numeric codes need to be identified!

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#250


All data

st_css() #IMPORTANT!
  birthplace <- as.factor(trimws(d[,"birthplace"]))
  # recode for interpretable birthplace
  #new.d.n <- data.frame(new.d.n, birthplace) # keep NAACCR coding
  levels(birthplace)[levels(birthplace)=="999"] <- "Unkown.999"
  levels(birthplace)[levels(birthplace)=="97"] <- "Sonoma (Greater California).97"
  levels(birthplace)[levels(birthplace)=="75"] <- "Kiowa.75"
  levels(birthplace)[levels(birthplace)=="73"] <- "Ouachita.73"
  levels(birthplace)[levels(birthplace)=="5"] <- "Bristol.5"
  levels(birthplace)[levels(birthplace)=="33"] <- "Burke.33"
  levels(birthplace)[levels(birthplace)=="11"] <- "Cayuga.11"
  levels(birthplace)[levels(birthplace)=="1"] <- "Appling.1"
  
  
  new.d <- data.frame(new.d, birthplace)
  new.d <- apply_labels(new.d, birthplace = "Place of birth")
  
  temp.d <- data.frame (new.d.1, birthplace)
  summarytools::view(dfSummary(new.d$birthplace, style = 'grid',
                               max.distinct.values = 100, plain.ascii =   FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace [labelled, factor] Place of birth 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater Californi 20. Unkown.999
131(14.6%)
6(0.7%)
1(0.1%)
3(0.3%)
14(1.6%)
1(0.1%)
3(0.3%)
1(0.1%)
1(0.1%)
51(5.7%)
5(0.6%)
3(0.3%)
4(0.4%)
1(0.1%)
1(0.1%)
1(0.1%)
19(2.1%)
1(0.1%)
1(0.1%)
652(72.4%)
 2657 (74.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater California).97 20. Unkown.999 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater Californi 20. Unkown.999
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
1(0.5%)
1(0.5%)
196(97.5%)
 9 (4.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater California).97 20. Unkown.999 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater Californi 20. Unkown.999
1(0.9%)
3(2.8%)
0(0.0%)
3(2.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
101(92.7%)
 247 (69.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater Californi 20. Unkown.999
8(3.6%)
2(0.9%)
0(0.0%)
0(0.0%)
14(6.2%)
1(0.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.4%)
0(0.0%)
1(0.4%)
0(0.0%)
0(0.0%)
17(7.6%)
0(0.0%)
0(0.0%)
181(80.4%)
 360 (61.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater Californi 20. Unkown.999
120(34.3%)
1(0.3%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
3(0.9%)
1(0.3%)
1(0.3%)
51(14.6%)
4(1.1%)
2(0.6%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
162(46.3%)
 1404 (80.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. 0 2. 000 3. 031 4. 041 5. 073 6. 100 7. Cayuga.11 8. 244 9. 25 10. Burke.33 11. 35 12. 37 13. 41 14. 43 15. 530 16. 63 17. Ouachita.73 18. 87 19. Sonoma (Greater Californi 20. Unkown.999
1(6.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(13.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
12(80.0%)
 1 (6.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


BIRTHPLACE–STATE


  • USPS abbreviation for the state, commonwealth, U.S. possession; or CanadaPost abbreviation for the Canadian province/territory in which the patient was born. If the patient has multiple primaries, the state of birth is the same for each tumor. This data item became part of the NAACCR transmission record effective with Volume II, Version 13 in order to include country and state for each geographic item and to use interoperable codes. It supplements the item BIRTHPLACE–COUNTRY [254]. These two data items are intended to replace the item BIRTHPLACE [250].

  • Rationale: This is a modification of the current item Birthplace [250] item in order to make use of standard codes, rather than using geographic codes that are only used by cancer registries. The intention is that item 250 be converted to populate the new corresponding, more standard, data items. Codes

  • See Appendix B for numeric and alphabetic lists of places and codes (also see Appendix B of the SEER Program Code Manual at seer.cancer.gov/tools/codingmanuals/index.html).

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#252


All data

st_css() #IMPORTANT!
  birthplacestate <- as.factor(trimws(d[,"birthplacestate"]))
  # recode for interpretable birthplace state
  #new.d.n <- data.frame(new.d.n, birthplacestate) # keep NAACCR coding
  levels(birthplacestate)[levels(birthplacestate)=="XX"] <- "Unknown.XX"
  levels(birthplacestate)[levels(birthplacestate)=="YY"] <- "Unknown.YY"
  levels(birthplacestate)[levels(birthplacestate)=="ZZ"] <- "Unknown.ZZ"

  new.d <- data.frame(new.d, birthplacestate)
  new.d <- apply_labels(new.d, birthplacestate = "State of birth")

temp.d <- data.frame (new.d.1, birthplacestate)
summarytools::view(dfSummary(new.d$birthplacestate, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 birthplacestate [labelled, factor] State of birth 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
3(0.1%)
25(0.7%)
5(0.1%)
2(0.1%)
103(2.9%)
1(0.0%)
3(0.1%)
1(0.0%)
17(0.5%)
310(8.7%)
1(0.0%)
1(0.0%)
5(0.1%)
5(0.1%)
5(0.1%)
5(0.1%)
164(4.6%)
1(0.0%)
27(0.8%)
1(0.0%)
5(0.1%)
12(0.3%)
5(0.1%)
4(0.1%)
14(0.4%)
10(0.3%)
4(0.1%)
6(0.2%)
15(0.4%)
4(0.1%)
14(0.4%)
596(16.8%)
1(0.0%)
9(0.3%)
4(0.1%)
55(1.5%)
5(0.1%)
2109(59.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
1(0.3%)
4(1.2%)
1(0.3%)
0(0.0%)
43(13.4%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
2(0.6%)
1(0.3%)
3(0.9%)
0(0.0%)
4(1.2%)
0(0.0%)
2(0.6%)
0(0.0%)
2(0.6%)
3(0.9%)
3(0.9%)
1(0.3%)
5(1.6%)
5(1.6%)
2(0.6%)
2(0.6%)
1(0.3%)
1(0.3%)
7(2.2%)
25(7.8%)
0(0.0%)
0(0.0%)
4(1.2%)
13(4.0%)
2(0.6%)
180(56.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
0(0.0%)
2(1.0%)
2(1.0%)
1(0.5%)
17(8.1%)
1(0.5%)
0(0.0%)
0(0.0%)
3(1.4%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.0%)
1(0.5%)
0(0.0%)
1(0.5%)
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
2(1.0%)
25(11.9%)
1(0.5%)
1(0.5%)
0(0.0%)
6(2.9%)
0(0.0%)
139(66.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
2(0.6%)
3(1.0%)
1(0.3%)
0(0.0%)
40(12.7%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
4(1.3%)
7(2.2%)
1(0.3%)
6(1.9%)
0(0.0%)
1(0.3%)
4(1.3%)
0(0.0%)
0(0.0%)
2(0.6%)
1(0.3%)
1(0.3%)
3(1.0%)
4(1.3%)
1(0.3%)
2(0.6%)
16(5.1%)
0(0.0%)
3(1.0%)
0(0.0%)
20(6.3%)
1(0.3%)
189(60.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
14(3.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
22(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
318(89.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
0(0.0%)
3(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
145(24.8%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
33(5.6%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.3%)
0(0.0%)
397(67.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
0(0.0%)
13(0.7%)
1(0.1%)
1(0.1%)
3(0.2%)
0(0.0%)
1(0.1%)
0(0.0%)
13(0.7%)
308(17.6%)
0(0.0%)
0(0.0%)
2(0.1%)
3(0.2%)
2(0.1%)
0(0.0%)
7(0.4%)
0(0.0%)
3(0.2%)
0(0.0%)
0(0.0%)
3(0.2%)
2(0.1%)
2(0.1%)
6(0.3%)
2(0.1%)
1(0.1%)
1(0.1%)
8(0.5%)
1(0.1%)
3(0.2%)
474(27.0%)
0(0.0%)
5(0.3%)
0(0.0%)
14(0.8%)
2(0.1%)
873(49.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_state [factor] 1. AK 2. AL 3. AR 4. AZ 5. CA 6. CD 7. CT 8. DE 9. FL 10. GA 11. GU 12. IA 13. IL 14. IN 15. KS 16. KY 17. LA 18. MA 19. MI 20. MN 21. MO 22. MS 23. NC 24. NJ 25. NY 26. OH 27. OK 28. PA 29. SC 30. TN 31. TX 32. US 33. UT 34. VA 35. WA 36. Unknown.XX 37. Unknown.YY 38. Unknown.ZZ
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(12.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
13(81.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


BIRTHPLACE–COUNTRY


  • Code for the country in which the patient was born. If the patient has multiple tumors, all records should contain the same code. This data item became part of the NAACCR transmission record effective with Volume II, Version 13 in order to include country and state for each geographic item and to use interoperable codes. It supplements the item BIRTHPLACE–STATE [252]. These two data items are intended to replace the use of BIRTHPLACE [250].

  • Rationale: Place of Birth is helpful for patient matching and can be used when reviewing race and ethnicity. It is an important item in algorithms for imputing race and ethnicity. In addition, adding birthplace data to race and ethnicity allows for a more specific definition of the population being reported. Careful descriptions of ancestry, birthplace, and immigration history of populations studied are needed to make the basis for classification into ethnic groups clear. Birthplace has been associated with variation in genetic, socioeconomic, cultural, and nutritional characteristics that affect patterns of disease. A better understanding of the differences within racial and ethnic categories also can help states develop effective, culturally-sensitive public health prevention programs to decrease the prevalence of high-risk behaviors and increase the use of preventive services.

  • See Appendix B for numeric and alphabetic lists of places and codes (also see Appendix B of the SEER Program Code Manual at seer.cancer.gov/tools/codingmanuals/index.html).

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#254


All data

st_css() #IMPORTANT!}
  birthplacecountry <- as.factor(trimws(d[,"birthplacecountry"]))
  #new.d.n <- data.frame(new.d.n, birthplacecountry) # keep NAACCR coding
  # recode for interpretable birthplace country
  levels(birthplacecountry)[levels(birthplacecountry)=="ZZU"] <- "Unknown.ZZU"
  levels(birthplacecountry)[levels(birthplacecountry)=="ZZF"] <- "Africa.ZZF"
  levels(birthplacecountry)[levels(birthplacecountry)=="ZZX"] <- "Non_US.ZZX"
  levels(birthplacecountry)[levels(birthplacecountry)=="ZZE"] <- "Europe NOS.ZZE"
  levels(birthplacecountry)[levels(birthplacecountry)=="ZZC"] <- "Central American NOS.ZZC"
  levels(birthplacecountry)[levels(birthplacecountry)=="ZWE"] <- "Zimbabwe.ZWE"
  levels(birthplacecountry)[levels(birthplacecountry)=="XWF"] <- "West Africa, NOS (French Africa, NOS).XWF"
  levels(birthplacecountry)[levels(birthplacecountry)=="WSM"] <- "Samoa.WSM"
  levels(birthplacecountry)[levels(birthplacecountry)=="VIR"] <- "Virgin Islands (U.S.).VIR"
  levels(birthplacecountry)[levels(birthplacecountry)=="USA"] <- "United States of America (the).USA"
  levels(birthplacecountry)[levels(birthplacecountry)=="UGA"] <- "Uganda.UGA"
  levels(birthplacecountry)[levels(birthplacecountry)=="TTO"] <- "Trinidad and Tobago.TTO"
  levels(birthplacecountry)[levels(birthplacecountry)=="SOM"] <- "Somalia.SOM"
  levels(birthplacecountry)[levels(birthplacecountry)=="SLE"] <- "Sierra Leone.SLE"
  levels(birthplacecountry)[levels(birthplacecountry)=="SEN"] <- "Senegal.SEN"
  levels(birthplacecountry)[levels(birthplacecountry)=="RWA"] <- "Rwanda.RWA"
  levels(birthplacecountry)[levels(birthplacecountry)=="PRT"] <- "Portugal.PRT"
  levels(birthplacecountry)[levels(birthplacecountry)=="PRI"] <- "Puerto Rico.PRI"
  levels(birthplacecountry)[levels(birthplacecountry)=="PAN"] <- "Panama.PAN"
  levels(birthplacecountry)[levels(birthplacecountry)=="NIC"] <- "Nicaragua.NIC"
  levels(birthplacecountry)[levels(birthplacecountry)=="NGA"] <- "Nigeria.NGA"
  levels(birthplacecountry)[levels(birthplacecountry)=="NER"] <- "Niger.NER"
  levels(birthplacecountry)[levels(birthplacecountry)=="MEX"] <- "Mexico.MEX"
  levels(birthplacecountry)[levels(birthplacecountry)=="LCA"] <- "Saint Lucia.LCA"
  levels(birthplacecountry)[levels(birthplacecountry)=="LBR"] <- "Liberia.LBR"
  levels(birthplacecountry)[levels(birthplacecountry)=="JPN"] <- "Japan.JPN"
  levels(birthplacecountry)[levels(birthplacecountry)=="JAM"] <- "Jamaica.JAM"
  levels(birthplacecountry)[levels(birthplacecountry)=="HUN"] <- "Hungary.HUN"
  levels(birthplacecountry)[levels(birthplacecountry)=="HTI"] <- "Haiti.HTI"
  levels(birthplacecountry)[levels(birthplacecountry)=="GUY"] <- "Guyana.GUY"
  levels(birthplacecountry)[levels(birthplacecountry)=="GUM"] <- "Guam.GUM"
  levels(birthplacecountry)[levels(birthplacecountry)=="GRD"] <- "Grenada.GRD"
  levels(birthplacecountry)[levels(birthplacecountry)=="GMB"] <- "Gambia (the).GMB"
  levels(birthplacecountry)[levels(birthplacecountry)=="GHA"] <- "Ghana.GHA"
  levels(birthplacecountry)[levels(birthplacecountry)=="GEO"] <- "Georgia.GEO"
  levels(birthplacecountry)[levels(birthplacecountry)=="GBR"] <- "United Kingdom of Great Britain and Northern Ireland (the).GBR"
  levels(birthplacecountry)[levels(birthplacecountry)=="FRA"] <- "France.FRA"
  levels(birthplacecountry)[levels(birthplacecountry)=="ETH"] <- "Ethiopia.ETH"
  levels(birthplacecountry)[levels(birthplacecountry)=="ERI"] <- "Eritrea.ERI"
  levels(birthplacecountry)[levels(birthplacecountry)=="ENG"] <- "England.ENG"
  levels(birthplacecountry)[levels(birthplacecountry)=="DOM"] <- "Dominican Republic (the).DOM"
  levels(birthplacecountry)[levels(birthplacecountry)=="DEU"] <- "Germany.DEU"
  levels(birthplacecountry)[levels(birthplacecountry)=="CUB"] <- "Cuba.CUB"
  levels(birthplacecountry)[levels(birthplacecountry)=="CRI"] <- "Costa Rica.CRI"
  levels(birthplacecountry)[levels(birthplacecountry)=="COG"] <- "Congo (the).COG"
  levels(birthplacecountry)[levels(birthplacecountry)=="CMR"] <- "Cameroon.CMR"
  levels(birthplacecountry)[levels(birthplacecountry)=="CIV"] <- "Cote d'Ivoire.CIV"
  levels(birthplacecountry)[levels(birthplacecountry)=="CAN"] <- "Canada.CAN"
  levels(birthplacecountry)[levels(birthplacecountry)=="BRB"] <- "Barbados.BRB"
  levels(birthplacecountry)[levels(birthplacecountry)=="BLZ"] <- "Belize.BLZ"
  levels(birthplacecountry)[levels(birthplacecountry)=="BHS"] <- "Bahamas (the).BHS"
  levels(birthplacecountry)[levels(birthplacecountry)=="ARE"] <- "United Arab Emirates (the).ARE"

  
  new.d <- data.frame(new.d, birthplacecountry)
  new.d <- apply_labels(new.d, birthplacecountry = "Country of birth")
  
temp.d <- data.frame (new.d.1, birthplacecountry)
summarytools::view(dfSummary(new.d$birthplacecountry, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 birthplacecountry [labelled, factor] Country of birth 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
10(0.3%)
1(0.0%)
1(0.0%)
1(0.0%)
2(0.1%)
17(0.5%)
1(0.0%)
1(0.0%)
1(0.0%)
3(0.1%)
1386(39.0%)
1(0.0%)
1(0.0%)
2110(59.3%)
2(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
3(0.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
3(0.9%)
1(0.3%)
0(0.0%)
0(0.0%)
2(0.6%)
125(38.9%)
0(0.0%)
1(0.3%)
181(56.4%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
2(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
64(30.5%)
0(0.0%)
0(0.0%)
139(66.2%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
11(3.5%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
105(33.3%)
0(0.0%)
0(0.0%)
189(60.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
38(10.7%)
0(0.0%)
0(0.0%)
318(89.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
186(31.8%)
0(0.0%)
0(0.0%)
397(67.9%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
1(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
3(0.2%)
1(0.1%)
1(0.1%)
1(0.1%)
0(0.0%)
2(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
0(0.0%)
865(49.3%)
1(0.1%)
0(0.0%)
873(49.8%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 birthplace_country [factor] 1. Bahamas (the).BHS 2. Belize.BLZ 3. Barbados.BRB 4. Canada.CAN 5. Cameroon.CMR 6. Cuba.CUB 7. Germany.DEU 8. England.ENG 9. Ethiopia.ETH 10. France.FRA 11. Georgia.GEO 12. Ghana.GHA 13. Gambia (the).GMB 14. Guam.GUM 15. Guyana.GUY 16. Haiti.HTI 17. Jamaica.JAM 18. KEN 19. Liberia.LBR 20. Saint Lucia.LCA 21. Niger.NER 22. Nigeria.NGA 23. Nicaragua.NIC 24. Panama.PAN 25. Senegal.SEN 26. Trinidad and Tobago.TTO 27. United States of America 28. Europe NOS.ZZE 29. Africa.ZZF 30. Unknown.ZZU 31. Non_US.ZZX
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
0(0.0%)
0(0.0%)
13(81.2%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CENSUS BLOCK GROUP 2000

  • Description: This field is provided for coding the block group of patient’s residence at time of diagnosis, as defined by the 2000 Census.
  • Rationale: A block group is a subdivision of a census tract designed to have an average of 1500 people, versus a census tract’s average of 4500 people. All land area in the United States is described by a census block group in the 2000 Census. The Census Bureau publishes detailed population and socioeconomic data at this level.
  • Block groups thus offer a high level of specificity for geographical and socioeconomic analyses. A block group has no meaning in the absence of a census tract. Refer to Census Tr Certainty 2000 [365] to ascertain basis of assignment of Census Block Group 2000.
  • Comment: Numerous registries find the distinction between “attempted, could not be determined” (zero) and “not coded” (blank) to be useful for geocoding planning purposes.
  • Note: The values 1 through 9 are nominal, with no hierarchy of values. This number determines the first digit of all the blocks which comprise the block group; for instance, census block group 3 would contain blocks numbered 3000 to 3999.
  • Codes
    • 0 Census block group assignment was attempted, but the value could not be determined
    • 1-9 Census block group values as defined by the Census Bureau
    • Blank Census Block Group 2000 not coded
  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#362


All data

st_css() #IMPORTANT!
  censusblockgroup2000 <- as.factor(trimws(d[,"censusblockgroup2000"]))

  new.d <- data.frame(new.d, censusblockgroup2000)
  new.d <- apply_labels(new.d, censusblockgroup2000 = "census_block_group_2000")

temp.d <- data.frame (new.d.1, censusblockgroup2000)
summarytools::view(dfSummary(new.d$censusblockgroup2000, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 censusblockgroup2000 [labelled, factor] census_block_group_2000 1. 1
1(100.0%)
 3556 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2000 [factor] 1. 1
1(100.0%)
 15 (93.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CENSUS BLOCK GROUP 2010

  • Description: This field is provided for coding the block group of patient’s residence at time of diagnosis, as defined by the 2010 Census.

  • Rationale: A block group is a subdivision of a census tract designed to have an average of 1500 people, versus a census tract’s average of 4500 people. All land area in the United States is described by a census block group in the 2010 Census. The Census Bureau publishes detailed population and socioeconomic data at this level. Block groups thus offer a high level of specificity for geographical and socioeconomic analyses.

  • A block group has no meaning in the absence of a census tract. Refer to Census Tr Certainty 2010 [367] to ascertain basis of assignment of Census Block Group 2010.

  • Comment: Numerous registries find the distinction between “attempted, could not be determined” (zero) and “not coded” (blank) to be useful for geocoding planning purposes.

  • Note: The values 1 through 9 are nominal, with no hierarchy of values. This number determines the first digit of all the blocks which comprise the block group; for instance, census block group 3 would contain blocks numbered 3000 to 3999.

  • Codes

    • 0 Census block group assignment was attempted, but the value could not be determined
    • 1-9 Census block group values as defined by the Census Bureau
    • Blank Census Block Group 2010 not coded

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#363


All data

st_css() #IMPORTANT!
  censusblockgroup2010 <- as.factor(trimws(d[,"censusblockgroup2010"]))

  new.d <- data.frame(new.d, censusblockgroup2010)
  new.d <- apply_labels(new.d, censusblockgroup2010 = "census_block_group_2010")

temp.d <- data.frame (new.d.1, censusblockgroup2010)
summarytools::view(dfSummary(new.d$censusblockgroup2010, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 censusblockgroup2010 [labelled, factor] census_block_group_2010 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
1124(34.7%)
873(26.9%)
577(17.8%)
420(13.0%)
100(3.1%)
15(0.5%)
3(0.1%)
1(0.0%)
128(3.9%)
 316 (8.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
96(30.0%)
103(32.2%)
76(23.8%)
34(10.6%)
7(2.2%)
3(0.9%)
1(0.3%)
0(0.0%)
0(0.0%)
 1 (0.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
79(37.6%)
65(31.0%)
41(19.5%)
23(11.0%)
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
118(33.1%)
118(33.1%)
69(19.4%)
36(10.1%)
12(3.4%)
1(0.3%)
2(0.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
183(31.3%)
0(0.0%)
15(2.6%)
214(36.6%)
44(7.5%)
0(0.0%)
0(0.0%)
1(0.2%)
128(21.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
641(36.5%)
582(33.2%)
373(21.3%)
113(6.4%)
35(2.0%)
10(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_block_group_2010 [factor] 1. 1 2. 2 3. 3 4. 4 5. 5 6. 6 7. 7 8. 8 9. 9
7(43.8%)
5(31.2%)
3(18.8%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CENSUS TR CERTAINTY 2000

  • Description: Code indicating basis of assignment of census tract for an individual record. Helpful in identifying cases tracted from incomplete information or P.O. Box. This item is not coded by the hospital. Central registry staff assign the code.
  • Codes
    • 1 Census tract based on complete and valid street address of residence
    • 2 Census tract based on residence ZIP + 4
    • 3 Census tract based on residence ZIP + 2
    • 4 Census tract based on residence ZIP code only
    • 5 Census tract based on ZIP code of P.O. Box
    • 6 Census tract/BNA based on residence city where city has only one census tract, or based on residence ZIP code where ZIP code has only one census tract
    • 9 Not assigned, geocoding attempted
    • Blank Not assigned, geocoding not attempted
  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#365


All data

st_css() #IMPORTANT!
  censustrcertainty2000 <- as.factor(trimws(d[,"censustrcertainty2000"]))
  levels(censustrcertainty2000)[levels(censustrcertainty2000)=="1"] <- "complete_and_valid.1"
  levels(censustrcertainty2000)[levels(censustrcertainty2000)=="9"] <- "Not_assigned.9"

  new.d <- data.frame(new.d, censustrcertainty2000)
  new.d <- apply_labels(new.d, censustrcertainty2000 = "census_tr_certainty_2000")

temp.d <- data.frame (new.d.1, censustrcertainty2000)
summarytools::view(dfSummary(new.d$censustrcertainty2000, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 censustrcertainty2000 [labelled, factor] census_tr_certainty_2000 1. complete_and_valid.1 2. Not_assigned.9
27(3.1%)
845(96.9%)
 2685 (75.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9
0(0.0%)
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9
0(0.0%)
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9
1(0.3%)
314(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9
19(100.0%)
0(0.0%)
 337 (94.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2000 [factor] 1. complete_and_valid.1 2. Not_assigned.9
7(100.0%)
0(0.0%)
 9 (56.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CENSUS TR CERTAINTY 2000

  • Description: Code indicating basis of assignment of census tract for an individual record. Helpful in identifying cases tracted from incomplete information or P.O. Box. This item is not coded by the hospital. Central registry staff assign the code.

  • Codes

    • 1 Census tract based on complete and valid street address of residence
    • 2 Census tract based on residence ZIP + 4
    • 3 Census tract based on residence ZIP + 2
    • 4 Census tract based on residence ZIP code only
    • 5 Census tract based on ZIP code of P.O. Box
    • 6 Census tract/BNA based on residence city where city has only one census tract, or based on residence ZIP code where ZIP code has only one census tract
    • 9 Not assigned, geocoding attempted
    • Blank Not assigned, geocoding not attempted

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#367


All data

st_css() #IMPORTANT!
  censustrcertainty2010 <- as.factor(trimws(d[,"censustrcertainty2010"]))
  levels(censustrcertainty2010)[levels(censustrcertainty2010)=="1"] <- "complete_and_valid.1"
  levels(censustrcertainty2010)[levels(censustrcertainty2010)=="2"] <- "ZIP_4.2"
  levels(censustrcertainty2010)[levels(censustrcertainty2010)=="4"] <- "ZIP_code_only.4"
  levels(censustrcertainty2010)[levels(censustrcertainty2010)=="5"] <- "ZIP_code_of_PO_Box.5"
  levels(censustrcertainty2010)[levels(censustrcertainty2010)=="9"] <- "Not_assigned.9"

  new.d <- data.frame(new.d, censustrcertainty2010)
  new.d <- apply_labels(new.d, censustrcertainty2010 = "census_tr_certainty_2010")

temp.d <- data.frame (new.d.1, censustrcertainty2010)
summarytools::view(dfSummary(new.d$censustrcertainty2010, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 censustrcertainty2010 [labelled, factor] census_tr_certainty_2010 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
3516(98.8%)
3(0.1%)
4(0.1%)
32(0.9%)
2(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
312(97.2%)
0(0.0%)
0(0.0%)
8(2.5%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
202(96.2%)
0(0.0%)
0(0.0%)
8(3.8%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
303(96.2%)
2(0.6%)
0(0.0%)
9(2.9%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
354(99.4%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
582(99.5%)
0(0.0%)
1(0.2%)
2(0.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
1747(99.6%)
1(0.1%)
2(0.1%)
4(0.2%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 census_tr_certainty_2010 [factor] 1. complete_and_valid.1 2. ZIP_4.2 3. ZIP_code_only.4 4. ZIP_code_of_PO_Box.5 5. Not_assigned.9
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SEQUENCE NUMBER–CENTRAL

  • Description: Code indicates the sequence of all reportable neoplasms over the lifetime of the person. This data item differs from Sequence Number-Hospital [560], because the definitions of reportable neoplasms often vary between a hospital and a central registry. Each neoplasm is assigned a different number. Sequence Number 00 indicates that the person has had only one in situ or one malignant neoplasm as defined by the Federal reportable list (regardless of central registry reference date). Sequence Number 01 indicates the first of two or more reportable neoplasms, but 02 indicates the second of two or more reportable neoplasms, and so on. Because the time period of Sequence Number is a person’s lifetime, reportable neoplasms not included in the central registry (those that occur outside the registry catchment area or before the reference date) also are allotted a sequence number. For example, a registry may contain a single record for a patient with a sequence number of 02 because the first reportable neoplasm preceded the central registry’s reference date.

  • Reporting Requirements: Federally Required and State/Province Defined: The Federal or SEER/NPCR standard defining the reportable neoplasms is described in Chapter III, Standards For Tumor Inclusion and Reportability. It is assumed that this shared standard is the “minimum” definition of reportability. Individual central cancer registries may define additional neoplasms as reportable.

  • Numeric codes in the 00-59 range indicate the sequence of neoplasms of in situ or malignant behavior (2 or 3) at the time of diagnosis, which SEER/NPCR standards require to be reported. Codes 60 to 87 indicate the sequence of non-malignant tumors (as defined in Chapter III) and any other neoplasms that the central registry has defined as reportable. Neoplasms required by SEER/NPCR with an in situ or malignant behavior at the time of diagnosis are sequenced completely independently of this higher-numbered category. Sequence Number-Hospital does not affect Sequence Number-Central. The two notational systems are independent but central registries should take Sequence Number-Hospital [560] into account when coding Sequence Number Central.

  • Rationale: The purpose of sequencing based on the patient’s lifetime is to truly identify the 00s, the people who only had one malignant primary in their lifetimes for survival analysis. If a central registry sequences by just what is reported to them, then it will be unclear whether 00 means the person only had one malignant primary in his lifetime or the person had one malignant primary since the central registry started collecting data. The Federally required reportable list has changed throughout the years, so the registry must use the appropriate reportable list for the year of diagnosis. The central registry reference date will not affect Sequence Number-Central.

  • Codes

    • 00 One primary in the patient’s lifetime
    • 01 First of two or more primaries
    • 02 Second of two or more primaries
    • 03 Third of two or more primaries
    • 04 Forth of two or more primaries

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#380


All data

st_css() #IMPORTANT!
  sequencenumbercentral <- as.factor(trimws(d[,"sequencenumbercentral"]))
  levels(sequencenumbercentral)[levels(sequencenumbercentral)=="0"] <- "One_primary.0"
  levels(sequencenumbercentral)[levels(sequencenumbercentral)=="1"] <- "First_of_two_or_more.1"
  levels(sequencenumbercentral)[levels(sequencenumbercentral)=="2"] <- "Second_of_two_or_more.2"
  levels(sequencenumbercentral)[levels(sequencenumbercentral)=="3"] <- "Third_of_two_or_more.2"
  levels(sequencenumbercentral)[levels(sequencenumbercentral)=="4"] <- "Forth_of_two_or_more.4"

  new.d <- data.frame(new.d, sequencenumbercentral)
  new.d <- apply_labels(new.d, sequencenumbercentral = "sequence_number_central")

temp.d <- data.frame (new.d.1, sequencenumbercentral)
summarytools::view(dfSummary(new.d$sequencenumbercentral, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 sequencenumbercentral [labelled, factor] sequence_number_central 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
2719(76.4%)
576(16.2%)
16(0.4%)
28(0.8%)
1(0.0%)
68(1.9%)
132(3.7%)
15(0.4%)
2(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
304(94.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(1.6%)
10(3.1%)
2(0.6%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
194(92.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(3.3%)
7(3.3%)
2(1.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
300(95.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
13(4.1%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
152(42.7%)
174(48.9%)
7(2.0%)
1(0.3%)
0(0.0%)
4(1.1%)
13(3.7%)
4(1.1%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
281(48.0%)
256(43.8%)
9(1.5%)
21(3.6%)
1(0.2%)
3(0.5%)
12(2.1%)
2(0.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
1473(84.0%)
146(8.3%)
0(0.0%)
6(0.3%)
0(0.0%)
46(2.6%)
77(4.4%)
5(0.3%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sequence_number_central [factor] 1. One_primary.0 2. 00 3. 01 4. 02 5. 03 6. First_of_two_or_more.1 7. Second_of_two_or_more.2 8. Third_of_two_or_more.2 9. Forth_of_two_or_more.4
15(93.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DATE OF DIAGNOSIS


  • Date of initial diagnosis by a recognized medical practitioner for the tumor being reported whether clinically or microscopically confirmed. See Chapter X for date format.

  • For more discussion on determining date of diagnosis, consult the SEER Program Coding and Staging Manual or CoC STORE manual.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=21#390


All data

  dateofdiagnosis <- trimws(d[,"dateofdiagnosis"])
  #new.d.n <- data.frame(new.d.n, dateofdiagnosis) # keep NAACCR coding
  
  select99 <- ifelse(is.na(dateofdiagnosis), F, substr(dateofdiagnosis, start=7, stop=8)=="99")
  dateofdiagnosis[select99] <- substr(dateofdiagnosis[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(dateofdiagnosis), F, nchar(trimws(dateofdiagnosis))==6)
  dateofdiagnosis[select6] <- paste(dateofdiagnosis[select6], "15", sep="")
  
  select4 <- ifelse(is.na(dateofdiagnosis), F, nchar(trimws(dateofdiagnosis))==4)
  dateofdiagnosis[select4] <- paste(dateofdiagnosis[select4], "0615", sep="")
  
  dateofdiagnosis <- as.Date(dateofdiagnosis, c("%Y%m%d"))
  new.d <- data.frame(new.d, dateofdiagnosis)
  new.d <- apply_labels(new.d, dateofdiagnosis = "Date of Diagnosis")
  temp.d <- data.frame (new.d.1, dateofdiagnosis)
summarytools::view(dfSummary(new.d$dateofdiagnosis, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 dateofdiagnosis [labelled, Date] Date of Diagnosis min : 2011-08-15 med : 2016-04-15 max : 2018-12-18 range : 7y 4m 3d 741 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] 1. 2015-01-15 2. 2015-02-15 3. 2015-03-15 4. 2015-04-15 5. 2015-05-15 6. 2015-06-15 7. 2015-07-15 8. 2015-08-15 9. 2015-09-15 10. 2015-10-15 11. 2015-11-15 12. 2015-12-15 13. 2016-01-15 14. 2016-02-15 15. 2016-03-15 16. 2016-04-15 17. 2016-05-15 18. 2016-06-15 19. 2016-07-15 20. 2016-08-15 21. 2016-09-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-01-15 26. 2017-02-15 27. 2017-03-15 28. 2017-04-15 29. 2017-05-15 30. 2017-06-15 31. 2017-07-15 32. 2017-08-15 33. 2017-09-15 34. 2017-10-15 35. 2017-11-15 36. 2017-12-15
8(2.5%)
4(1.2%)
9(2.8%)
9(2.8%)
7(2.2%)
8(2.5%)
11(3.4%)
13(4.0%)
8(2.5%)
4(1.2%)
9(2.8%)
12(3.7%)
11(3.4%)
8(2.5%)
18(5.6%)
22(6.9%)
13(4.0%)
17(5.3%)
10(3.1%)
18(5.6%)
12(3.7%)
9(2.8%)
7(2.2%)
11(3.4%)
2(0.6%)
4(1.2%)
6(1.9%)
5(1.6%)
3(0.9%)
7(2.2%)
7(2.2%)
7(2.2%)
4(1.2%)
2(0.6%)
7(2.2%)
9(2.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] min : 2015-01-05 med : 2016-03-16 max : 2017-12-21 range : 2y 11m 16d 177 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] min : 2015-01-15 med : 2016-02-15 max : 2016-12-15 range : 1y 11m 0d 24 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] min : 2012-01-15 med : 2016-07-15 max : 2018-12-15 range : 6y 11m 0d 75 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] min : 2015-01-01 med : 2016-07-15 max : 2018-12-18 range : 3y 11m 17d 373 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] min : 2015-01-01 med : 2016-03-14 max : 2018-09-26 range : 3y 8m 25d 604 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_diagnosis [Date] min : 2011-08-15 med : 2013-05-15 max : 2015-04-15 range : 3y 8m 0d 15 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


PRIMARY SITE


  • Code for the primary site of the tumor being reported using either ICD-O-2 or ICD-O-3. NAACCR adopted ICD-O-2 as the standard coding system for tumors diagnosed beginning January 1, 1992. In addition, NAACCR recommended that tumors diagnosed prior to 1992 be converted to ICD-O-2. The topography (primary site) codes did not change between ICD-O-2 and ICD-O-3.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#400


  primarysite <- as.factor(d[,"primarysite"])
  levels(primarysite) <- list(Prostate="C619") # other codings become NA
  new.d <- data.frame(new.d, primarysite)
  new.d <- apply_labels(new.d, primarysite = "Primary Tumor Site")
  
  #cro(new.d$primarysite)# this is pretty but doesn't show NAs
  primarysite<-count(new.d$primarysite)
  colnames(primarysite)<- c("Primary site", "Total")
  kable(primarysite, format = "simple", align = 'l', caption = "Overview of 8 Registries")
Overview of 8 Registries
Primary site Total
Prostate 3556
NA 1


GRADE


  • Code for the grade or degree of differentiation of the reportable tumor. For lymphomas and leukemias, field also is used to indicate T-, B-, Null-, or NK-cell origin.

  • See the grade tables on page 67 of ICD-O-3.16 See also the most recent CoC STORE manual and SEER Program Code Manual, for site specific coding rules and conversions.

    • Grade I
    • Grade II
    • Grade III
    • Grade IV
    • T-cell
    • B-cell
    • Null cell
    • NK (natural killer) cell
    • Grade/differentiation unknown, not stated, or not applicable

  • Comment: Use the most recent Hematopoietic and Lymphoid rules for assigning grades 5-8.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#440

All data

  grade <- as.factor(d[,"grade"])
  levels(grade) <- list(Grade_I.1="1",
                        Grade_II.2="2",
                        Grade_III.3="3",
                        Grade_IV.4="4",
                        T_cell.5="5",
                        B_cell.6="6",
                        Null_cell.7="7",
                        NK_cell.8="8",
                        Unknown.9="9")
  new.d <- data.frame(new.d, grade)
  new.d <- apply_labels(new.d, grade = "Tumor Grade")
  
  #cro(new.d$grade)# this is pretty but doesn't show NAs
  #summary(new.d$grade)
temp.d <- data.frame (new.d.1, grade)
summarytools::view(dfSummary(new.d$grade, style = 'grid', max.distinct.values = 10, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 grade [labelled, factor] Tumor Grade 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
911(26.1%)
1774(50.8%)
771(22.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
37(1.1%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
89(27.7%)
161(50.2%)
68(21.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(0.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
68(32.4%)
112(53.3%)
28(13.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
83(26.3%)
153(48.6%)
75(23.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
40(13.6%)
166(56.3%)
87(29.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.7%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
135(23.1%)
297(50.9%)
148(25.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(0.7%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
494(28.2%)
878(50.1%)
358(20.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
22(1.3%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade [factor] 1. Grade_I.1 2. Grade_II.2 3. Grade_III.3 4. Grade_IV.4 5. T_cell.5 6. B_cell.6 7. Null_cell.7 8. NK_cell.8 9. Unknown.9
2(12.5%)
7(43.8%)
7(43.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DIAGNOSTIC CONFIRMATION

  • Description: Code for the best method of diagnostic confirmation of the cancer being reported at any time in the patient’s history.
  • Rationale: Diagnostic confirmation is useful to calculate rates based on microscopically confirmed cancers. Full incidence calculations must also include tumors that are only confirmed clinically. The percentage of tumors that not micropscopically confirmed is an indication of whether case finding is including sources outside of pathology reports.
  • Codes
    • 1 Positive histology
    • 2 Positive cytology
    • 3 Positive histology PLUS - positive immunophenotyping AND/OR positive genetic studies (Used only for hematopoietic and lymphoid neoplasms M-9590/3-9992/3)
    • 4 Positive microscopic confirmation, method not specified
    • 5 Positive laboratory test/marker study
    • 6 Direct visualization without microscopic confirmation
    • 7 Radiography and/or other imaging techniques without microscopic confirmation
    • 8 Clinical diagnosis only (other than 5, 6, or 7)
    • 9 Unknown whether or not microscopically confirmed; death certificate only
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#490

All data

st_css() #IMPORTANT!
  diagnosticconfirmation <- as.factor(trimws(d[,"diagnosticconfirmation"]))
  levels(diagnosticconfirmation)[levels(diagnosticconfirmation)=="1"] <- "Positive_histology.1"

  new.d <- data.frame(new.d, diagnosticconfirmation)
  new.d <- apply_labels(new.d, diagnosticconfirmation = "diagnostic_confirmation")

temp.d <- data.frame (new.d.1, diagnosticconfirmation)
summarytools::view(dfSummary(new.d$diagnosticconfirmation, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 diagnosticconfirmation [labelled, factor] diagnostic_confirmation 1. Positive_histology.1
3557(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
356(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
585(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
1754(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 diagnostic_confirmation [factor] 1. Positive_histology.1
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TYPE OF REPORTING SOURCE


- This variable codes the source documents used to abstract the majority of information on the tumor being reported. This may not be the source of original case finding (for example, if a case is identified through a pathology laboratory report review and all source documents used to abstract the case are from the physician’s office, code this item 4).

  • Rationale: The code in this field can be used to explain why information may be incomplete on a tumor. For example, death certificate only cases have unknown values for many data items, so one may want to exclude them from some analyses. The field also is used to monitor the success of non-hospital case reporting and follow-back mechanisms. All population-based registries should have some death certificate-only cases where no hospital admission was involved, but too high a percentage can imply both shortcomings in case-finding and that follow-back to uncover missed hospital reports was not complete.

  • Coding Instructions: Code in the following priority order: 1, 2, 8, 4, 3, 5, 6, 7. This is a change to reflect the addition of codes 2 and 8 and to prioritize laboratory reports over nursing home reports. The source facilities included in the previous code 1 (hospital inpatient and outpatient) are split between codes 1, 2, and 8.

  • This data item is intended to indicate the completeness of information available to the abstractor. Reports from health plans (e.g., Kaiser, Veterans Administration, military facilities) in which all diagnostic and treatment information is maintained centrally and is available to the abstractor are expected to be at least as complete as reports for hospital inpatients, which is why these sources are grouped with inpatients and given the code with the highest priority.

  • Sources coded with ‘2’ usually have complete information on the cancer diagnosis, staging, and treatment.

  • Sources coded with ‘8’ would include, but would not be limited to, outpatient surgery and nuclear medicine services. A physician’s office that calls itself a surgery center should be coded as a physician’s office. Surgery centers are equipped and staffed to perform surgical procedures under general anesthesia. If a physician’s office calls itself a surgery center, but cannot perform surgical procedures under general anesthesia, code as a physician office.

  • Codes

    • 1 Hospital inpatient; Managed health plans with comprehensive, unified medical records
    • 2 Radiation Treatment Centers or Medical Oncology Centers (hospital-affiliated or independent)
    • 3 Laboratory only (hospital-affiliated or independent)
    • 4 Physician’s office/private medical practitioner (LMD)
    • 5 Nursing/convalescent home/hospice
    • 6 Autopsy only
    • 7 Death certificate only
    • 8 Other hospital outpatient units/surgery centers

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#500


All data

  typeofreportingsource <- as.factor(d[,"typeofreportingsource"])
  levels(typeofreportingsource) <- list(Hospital.1="1",
                                 Radiation_Tx.2="2",
                                 Laboratory_Only.3="3",
                                 Physician.4="4",
                                 Nursing.5="5",
                                 Autopsy.6="6",
                                 Death_Certificate.7="7",
                                 Other_Hospital.Unit.8="8")
  new.d <- data.frame(new.d, typeofreportingsource)
  new.d <- apply_labels(new.d, typeofreportingsource = "Source of Tumor Information")
  
  #summary(new.d$typeofreportingsource)

temp.d <- data.frame (new.d.1, typeofreportingsource)
summarytools::view(dfSummary(new.d$typeofreportingsource, style = 'grid', max.distinct.values = 10, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 typeofreportingsource [labelled, factor] Source of Tumor Information 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
3184(89.5%)
126(3.5%)
139(3.9%)
58(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
50(1.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
317(98.8%)
0(0.0%)
0(0.0%)
4(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
203(96.7%)
3(1.4%)
0(0.0%)
4(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
304(96.5%)
8(2.5%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
342(96.1%)
0(0.0%)
11(3.1%)
3(0.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
507(86.7%)
14(2.4%)
29(5.0%)
34(5.8%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
1496(85.3%)
101(5.8%)
99(5.6%)
10(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
48(2.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 type_of_reporting_source [factor] 1. Hospital.1 2. Radiation_Tx.2 3. Laboratory_Only.3 4. Physician.4 5. Nursing.5 6. Autopsy.6 7. Death_Certificate.7 8. Other_Hospital.Unit.8
15(93.8%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


HISTOLOGIC TYPE ICD-O-3


All data

st_css() #IMPORTANT!
  histologictypeicdo3 <- as.factor(trimws(d[,"histologictypeicdo3"]))
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8000"] <- "Neoplasm_malignant.8000"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8010"] <- "Carcinoma_NOS.8010"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8140"] <- "Adenocarcinoma_NOS.8140"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8323"] <- "Mixed_cell_adenocarcinoma.8323"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8480"] <- "Mucinous_adenocarcinoma.8480 "
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8481"] <- "Mucin_producing_adenocarcinoma.8481"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8490"] <- "Signet_ring_cell_adenocarcinoma.8490"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8500"] <- "Invasive_breast_carcinoma.8500"
  levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8550"] <- "Acinar_cell_tumor.8550"

  new.d <- data.frame(new.d, histologictypeicdo3)
  new.d <- apply_labels(new.d, histologictypeicdo3 = "histologic_type_icdo3")

temp.d <- data.frame (new.d.1, histologictypeicdo3)
summarytools::view(dfSummary(new.d$histologictypeicdo3, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 histologictypeicdo3 [labelled, factor] histologic_type_icdo3 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
8(0.2%)
8(0.2%)
3515(98.8%)
1(0.0%)
1(0.0%)
1(0.0%)
3(0.1%)
4(0.1%)
15(0.4%)
1(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
0(0.0%)
0(0.0%)
317(98.8%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
2(0.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
0(0.0%)
0(0.0%)
210(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
1(0.3%)
0(0.0%)
313(99.4%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
0(0.0%)
1(0.3%)
352(98.9%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
0(0.0%)
0(0.0%)
578(98.8%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
6(1.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
7(0.4%)
7(0.4%)
1729(98.6%)
1(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
9(0.5%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 histologic_type_icdo3 [factor] 1. Neoplasm_malignant.8000 2. Carcinoma_NOS.8010 3. Adenocarcinoma_NOS.8140 4. Mixed_cell_adenocarcinoma 5. Mucinous_adenocarcinoma.8 6. Mucin_producing_adenocarc 7. Signet_ring_cell_adenocar 8. Invasive_breast_carcinoma 9. Acinar_cell_tumor.8550 10. 8552
0(0.0%)
0(0.0%)
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


BEHAVIOR CODE ICD-O-3

  • Description: Code for the behavior of the tumor being reported using ICD-O-3. NAACCR adopted ICD-O-3 as the standard coding system for tumors diagnosed beginning January 1, 2001, and later recommended that prior cases be converted from ICD-O-2. See Behavior (92-00) ICD-O-2 [430], for ICD-O-2 codes.

  • Juvenile astrocytoma is coded as borderline in ICD-O-3; North American registries report as 9421/3. Clarification of Required Status Behavior is required by all standard-setting organizations for tumors diagnosed on or after January 1, 2001, and recommended (by conversion from ICD-O-2 codes) for tumors diagnosed before 2001.

  • When the histologic type is coded according to the ICD-O-3, the histology code must be reported in Histologic Type ICD-O-3 [522], with behavior coded in Behavior Code ICD-O-3 [523].

  • For information on required status for related data items for histologic type and behavior when coded according to ICD-O-2, see Histology (92-00) ICD-O-2 [420] and Behavior (92-00) ICD-O-2 [430].

  • Codes

    • Valid codes are 0-3. See ICD-O-3,14 page 66, for behavior codes and definitions.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#523


All data

st_css() #IMPORTANT!
  behaviorcodeicdo3 <- as.factor(trimws(d[,"behaviorcodeicdo3"]))

  new.d <- data.frame(new.d, behaviorcodeicdo3)
  new.d <- apply_labels(new.d, behaviorcodeicdo3 = "behavior_code_icdo3")

temp.d <- data.frame (new.d.1, behaviorcodeicdo3)
summarytools::view(dfSummary(new.d$behaviorcodeicdo3, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 behaviorcodeicdo3 [labelled, factor] behavior_code_icdo3 1. 3
3557(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


PRIMARY PAYER AT DX

  • Description: Primary payer/insurance carrier at the time of initial diagnosis and/or treatment at the reporting facility.

  • Rationale: This item is used in financial analysis and as an indicator for quality and outcome analyses.

-Codes
+ 01 Not insured
+ 02 Not insured, self-pay
+ 10 Insurance, NOS
+ 20 Private Insurance: Managed care, HMO, or PPO
+ 21 Private Insurance: Fee-for-Service
+ 31 Medicaid
+ 35 Medicaid - Administered through a Managed Care plan
+ 60 Medicare/Medicare, NOS
+ 61 Medicare with supplement, NOS
+ 62 Medicare - Administered through a Managed Care plan
+ 63 Medicare with private supplement
+ 64 Medicare with Medicaid eligibility
+ 65 TRICARE
+ 66 Military
+ 67 Veterans Affairs
+ 68 Indian/Public Health Service
+ 99 Insurance status unknown


All data

  primarypayeratdx <- as.factor(d[,"primarypayeratdx"])
  levels(primarypayeratdx) <- list(Not_insured.1="1",
                               Not_insured_self_pay.2="2",
                               Insurance.10="10",
                               Private_managed.20="20",
                               Private_FFS.21="21", # fee-for-service
                               Medicaid.31="31",
                               Medicaid_managed_care.35="35",
                               Medicare_medicare.60="60",
                               Medicare_suppl.61="61",
                               Medicare_managed_care.62="62",
                               Medicare_private_suppl.63="63",
                               Medicare_medicaid.64="64",
                               TRICARE.65="65",
                               Military.66="66",
                               Veterans_Affairs.67="67",
                               Indian_PHS.68="68",
                               Unknown.99="99")
    primarypayeratdx <- relevel(primarypayeratdx, ref="Private_managed.20")
    new.d <- data.frame(new.d, primarypayeratdx)
    new.d <- apply_labels(new.d, primarypayeratdx = "Primary payer/insurance at the time of diagnosis")
    #summary(new.d$primarypayeratdx)
temp.d <- data.frame (new.d.1, primarypayeratdx)
summarytools::view(dfSummary(new.d$primarypayeratdx, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 primarypayeratdx [labelled, factor] Primary payer/insurance at the time of diagnosis 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
1435(41.1%)
0(0.0%)
0(0.0%)
86(2.5%)
63(1.8%)
118(3.4%)
108(3.1%)
282(8.1%)
166(4.8%)
328(9.4%)
190(5.4%)
81(2.3%)
93(2.7%)
3(0.1%)
223(6.4%)
0(0.0%)
316(9.0%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
197(62.1%)
0(0.0%)
0(0.0%)
6(1.9%)
1(0.3%)
12(3.8%)
13(4.1%)
17(5.4%)
6(1.9%)
40(12.6%)
6(1.9%)
7(2.2%)
0(0.0%)
0(0.0%)
7(2.2%)
0(0.0%)
5(1.6%)
 4 (1.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
131(62.7%)
0(0.0%)
0(0.0%)
4(1.9%)
0(0.0%)
9(4.3%)
6(2.9%)
21(10.0%)
5(2.4%)
12(5.7%)
1(0.5%)
9(4.3%)
1(0.5%)
0(0.0%)
5(2.4%)
0(0.0%)
5(2.4%)
 1 (0.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
176(55.9%)
0(0.0%)
0(0.0%)
3(1.0%)
1(0.3%)
16(5.1%)
13(4.1%)
21(6.7%)
28(8.9%)
18(5.7%)
3(1.0%)
6(1.9%)
6(1.9%)
0(0.0%)
4(1.3%)
0(0.0%)
20(6.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
124(35.3%)
0(0.0%)
0(0.0%)
15(4.3%)
3(0.9%)
12(3.4%)
33(9.4%)
26(7.4%)
12(3.4%)
38(10.8%)
49(14.0%)
14(4.0%)
1(0.3%)
0(0.0%)
4(1.1%)
0(0.0%)
20(5.7%)
 5 (1.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
199(35.1%)
0(0.0%)
0(0.0%)
18(3.2%)
7(1.2%)
24(4.2%)
29(5.1%)
50(8.8%)
29(5.1%)
78(13.8%)
15(2.6%)
8(1.4%)
6(1.1%)
1(0.2%)
66(11.6%)
0(0.0%)
37(6.5%)
 18 (3.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
598(34.8%)
0(0.0%)
0(0.0%)
39(2.3%)
51(3.0%)
45(2.6%)
13(0.8%)
146(8.5%)
84(4.9%)
142(8.3%)
116(6.8%)
37(2.2%)
79(4.6%)
2(0.1%)
137(8.0%)
0(0.0%)
229(13.3%)
 36 (2.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 primary_payer_at_dx [factor] 1. Private_managed.20 2. Not_insured.1 3. Not_insured_self_pay.2 4. Insurance.10 5. Private_FFS.21 6. Medicaid.31 7. Medicaid_managed_care.35 8. Medicare_medicare.60 9. Medicare_suppl.61 10. Medicare_managed_care.62 11. Medicare_private_suppl.63 12. Medicare_medicaid.64 13. TRICARE.65 14. Military.66 15. Veterans_Affairs.67 16. Indian_PHS.68 17. Unknown.99
10(66.7%)
0(0.0%)
0(0.0%)
1(6.7%)
0(0.0%)
0(0.0%)
1(6.7%)
1(6.7%)
2(13.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1 (6.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SEER SUMMARY STAGE 2000

  • Description: Code for summary stage at the initial diagnosis or treatment of the reportable tumor. For hospital registries, CoC requires its use in the absence of a defined AJCC classification. For site-specific definitions of categories, see SEER Summary Staging Manual 2000.

  • Summary stage should include all information available through completion of surgery(ies) in the first course of treatment or within 4 months of diagnosis in the absence of disease progression, whichever is longer.

  • Rationale: Stage information is important when evaluating the effects of cancer control programs. It is crucial in understanding whether changes over time in incidence rates or outcomes are due to earlier detection of the cancers. In addition, cancer treatment cannot be studied without knowing the stage at diagnosis.

  • Codes

    • 0 In situ
    • 1 Localized
    • 2 Regional, direct extension only
    • 3 Regional, regional lymph nodes only
    • 4 Regional, direct extension and regional lymph nodes
    • 5 Regional, NOS
    • 7 Distant
    • 8 Not applicable
    • 9 Unstaged

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#759


All data

st_css() #IMPORTANT!
  seersummarystage2000 <- as.factor(trimws(d[,"seersummarystage2000"]))
  levels(seersummarystage2000)[levels(seersummarystage2000)=="1"] <- "Localized.1"
  levels(seersummarystage2000)[levels(seersummarystage2000)=="2"] <- "Regional_direct_extension.2"
  levels(seersummarystage2000)[levels(seersummarystage2000)=="3"] <- "Regional_lymph_nodes.3"
  levels(seersummarystage2000)[levels(seersummarystage2000)=="4"] <- "Regional_both_23.4"
  levels(seersummarystage2000)[levels(seersummarystage2000)=="5"] <- "Regional_NOS.5"
  levels(seersummarystage2000)[levels(seersummarystage2000)=="7"] <- "Distant.7"
  levels(seersummarystage2000)[levels(seersummarystage2000)=="9"] <- "Unstaged.9"


  new.d <- data.frame(new.d, seersummarystage2000)
  new.d <- apply_labels(new.d, seersummarystage2000 = "seer_summary_stage_2000")

temp.d <- data.frame (new.d.1, seersummarystage2000)
summarytools::view(dfSummary(new.d$seersummarystage2000, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 seersummarystage2000 [labelled, factor] seer_summary_stage_2000 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
2772(81.0%)
365(10.7%)
37(1.1%)
61(1.8%)
3(0.1%)
74(2.2%)
109(3.2%)
 136 (3.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
249(77.6%)
41(12.8%)
2(0.6%)
11(3.4%)
0(0.0%)
10(3.1%)
8(2.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
170(81.0%)
27(12.9%)
3(1.4%)
3(1.4%)
0(0.0%)
6(2.9%)
1(0.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
238(75.6%)
40(12.7%)
7(2.2%)
13(4.1%)
0(0.0%)
13(4.1%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
177(74.4%)
37(15.5%)
1(0.4%)
11(4.6%)
0(0.0%)
7(2.9%)
5(2.1%)
 118 (33.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
465(79.6%)
87(14.9%)
6(1.0%)
6(1.0%)
1(0.2%)
9(1.5%)
10(1.7%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
1472(84.0%)
133(7.6%)
18(1.0%)
17(1.0%)
2(0.1%)
29(1.7%)
81(4.6%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_2000 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_lymph_nodes.3 4. Regional_both_23.4 5. Regional_NOS.5 6. Distant.7 7. Unstaged.9
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 15 (93.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SEER SUMMARY STAGE 1977

  • Description: Code for summary stage at the initial diagnosis or treatment of the reportable tumor. This has traditionally been used by central registries to monitor time trends. For hospital registries, CoC requires its use in the absence of a defined AJCC classification. For site-specific definitions of categories, see the SEER Summary Staging Guide.

  • SEER Summary Stage 1977 is limited to information available within 2 months of the date of diagnosis. NAACCR approved extension of this time period to 4 months for prostate tumors diagnosed beginning January 1, 1995.

  • Rationale: Stage information is important when evaluating the effects of cancer control programs. It is crucial for understanding whether changes over time in incidence rates or outcomes are due to earlier detection of the cancers. In addition, cancer treatment cannot be studied without knowing the stage at diagnosis.

  • To study historical trends in stage, the coding system must be relatively unchanged (stable) over time. AJCC’s TNM system is updated periodically to maintain clinical relevance with changes in diagnosis and treatment. The surveillance registries often rely on the Summary Stage, which they consider to be more “stable.” Summary Stage has been in widespread use, either as the primary staging scheme or a secondary scheme, in most central and hospital registries since 1977.

  • Codes

    • 9 Unstaged
    • 0 In situ
    • 1 Localized
    • 2 Regional, direct extension only
    • 3 Regional, regional lymph nodes only
    • 4 Regional, direct extension and regional lymph nodes
    • 5 Regional, NOS
    • 7 Distant
    • 8 Not applicable

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#760


All data

st_css() #IMPORTANT!
  seersummarystage1977 <- as.factor(trimws(d[,"seersummarystage1977"]))
  levels(seersummarystage1977)[levels(seersummarystage1977)=="1"] <- "Localized.1"
  levels(seersummarystage1977)[levels(seersummarystage1977)=="2"] <- "Regional_direct_extension.2"
  levels(seersummarystage1977)[levels(seersummarystage1977)=="4"] <- "Regional_both.4"
  levels(seersummarystage1977)[levels(seersummarystage1977)=="9"] <- "Unstaged.9"


  new.d <- data.frame(new.d, seersummarystage1977)
  new.d <- apply_labels(new.d, seersummarystage1977 = "seer_summary_stage_1977")

temp.d <- data.frame (new.d.1, seersummarystage1977)
summarytools::view(dfSummary(new.d$seersummarystage1977, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 seersummarystage1977 [labelled, factor] seer_summary_stage_1977 1. Localized.1 2. Regional_direct_extension 3. Regional_both.4 4. Unstaged.9
13(4.1%)
4(1.3%)
1(0.3%)
297(94.3%)
 3242 (91.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension.2 3. Regional_both.4 4. Unstaged.9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension.2 3. Regional_both.4 4. Unstaged.9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension 3. Regional_both.4 4. Unstaged.9
13(4.1%)
4(1.3%)
1(0.3%)
297(94.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension.2 3. Regional_both.4 4. Unstaged.9 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension.2 3. Regional_both.4 4. Unstaged.9 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension.2 3. Regional_both.4 4. Unstaged.9 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 seer_summary_stage_1977 [factor] 1. Localized.1 2. Regional_direct_extension.2 3. Regional_both.4 4. Unstaged.9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SUMMARY STAGE 2018

  • Description: Derived Summary Stage 2018 is derived using the EOD data collection system (EOD Primary Tumor [772], EOD Regional Nodes [774] and EOD Mets [776]) algorithm. Other data items may be included in the derivation process. Effective for cases diagnosed 1/1/2018+.

  • Rationale: The SEER program has collected staging information on cases since its inception in 1973. Summary Stage groups cases into broad categories of in situ, local, regional, and distant. Summary Stage can be used to evaluate disease spread at diagnosis, treatment patterns and outcomes over time.

  • Derived Summary Stage 2018 [762] is only available at the central registry level. Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.

  • Codes

    • 0 In situ
    • 1 Localized
    • 2 Regional, direct extension only
    • 3 Regional, regional lymph nodes only
    • 4 Regional, direct extension and regional lymph nodes
    • 7 Distant
    • 8 Benign, borderline
    • 9 Unknown if extension or metastasis (unstaged, unknown, or unspecified)/Death certificate only case

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#762


All data

  derivedsummarystage2018 <- as.factor(d[,"derivedsummarystage2018"])
  levels(derivedsummarystage2018) <- list(In_situ.0="0",
                                          Localized.1="1",
                                          Regional_direct.2="2",
                                          Regional_regional.3="3",
                                          Regional_direct_regional.4="4",
                                          Distant.7="7",
                                          Benign_borderline.8="8",
                                          Unknown.9="9")
    derivedsummarystage2018 <- relevel(derivedsummarystage2018, ref="Localized.1")
    new.d <- data.frame(new.d, derivedsummarystage2018)
    new.d <- apply_labels(new.d, derivedsummarystage2018 = "Tumor Staging")
    #summary(new.d$derivedsummarystage2018)
temp.d <- data.frame (new.d.1, derivedsummarystage2018)
summarytools::view(dfSummary(new.d$derivedsummarystage2018, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedsummarystage2018 [labelled, factor] Tumor Staging 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional. 6. Distant.7 7. Benign_borderline.8 8. Unknown.9
52(81.2%)
0(0.0%)
8(12.5%)
0(0.0%)
0(0.0%)
4(6.2%)
0(0.0%)
0(0.0%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional.4 6. Distant.7 7. Benign_borderline.8 8. Unknown.9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional.4 6. Distant.7 7. Benign_borderline.8 8. Unknown.9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional.4 6. Distant.7 7. Benign_borderline.8 8. Unknown.9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional. 6. Distant.7 7. Benign_borderline.8 8. Unknown.9
49(80.3%)
0(0.0%)
8(13.1%)
0(0.0%)
0(0.0%)
4(6.6%)
0(0.0%)
0(0.0%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional. 6. Distant.7 7. Benign_borderline.8 8. Unknown.9
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional. 6. Distant.7 7. Benign_borderline.8 8. Unknown.9
2(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_summary_stage_2018 [factor] 1. Localized.1 2. In_situ.0 3. Regional_direct.2 4. Regional_regional.3 5. Regional_direct_regional.4 6. Distant.7 7. Benign_borderline.8 8. Unknown.9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SUMMARY STAGE 2018

  • Description: This item stores the directly assigned Summary Stage 2018. Effective for cases diagnosed 1/1/2018+.
  • Rationale: The SEER program has collected staging information on cases since its inception in 1973. Summary Stage groups cases into broad categories of in situ, local, regional, and distant. Summary Stage can be used to evaluate disease spread at diagnosis, treatment patterns and outcomes over time.
  • Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
  • Codes
    • 0 In situ
    • 1 Localized only
    • 2 Regional by direct extension only
    • 3 Regional lymph nodes only
    • 4 Regional by BOTH direct extension AND lymph node involvement
    • 7 Distant site(s)/node(s) involved
    • 8 Benign/borderline*
    • 9 Unknown if extension or metastasis (unstaged, unknown, or unspecified) Death certificate only case
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#764


All data

st_css() #IMPORTANT!
  summarystage2018 <- as.factor(trimws(d[,"summarystage2018"]))
  levels(summarystage2018)[levels(summarystage2018)=="1"] <- "Localized_only.1"
  levels(summarystage2018)[levels(summarystage2018)=="2"] <- "Regional_direct_extension.2"
  levels(summarystage2018)[levels(summarystage2018)=="7"] <- "Distant_site_node_involved.7"


  new.d <- data.frame(new.d, summarystage2018)
  new.d <- apply_labels(new.d, summarystage2018 = "summary_stage_2018")

temp.d <- data.frame (new.d.1, summarystage2018)
summarytools::view(dfSummary(new.d$summarystage2018, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 summarystage2018 [labelled, factor] summary_stage_2018 1. Localized_only.1 2. Regional_direct_extension 3. Distant_site_node_involve
52(81.2%)
8(12.5%)
4(6.2%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension.2 3. Distant_site_node_involved.7 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension.2 3. Distant_site_node_involved.7 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension.2 3. Distant_site_node_involved.7 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension 3. Distant_site_node_involve
49(80.3%)
8(13.1%)
4(6.6%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension 3. Distant_site_node_involve
1(100.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension 3. Distant_site_node_involve
2(100.0%)
0(0.0%)
0(0.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 summary_stage_2018 [factor] 1. Localized_only.1 2. Regional_direct_extension.2 3. Distant_site_node_involved.7 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD PRIMARY TUMOR

  • Description: EOD Primary Tumor is part of the EOD 2018 data collection system and is used to classify contiguous growth (extension) of the primary tumor within the organ of origin or its direct extension into neighboring organs. See also EOD Regional Nodes [774] and EOD Mets [776]. Effective for cases diagnosed 1/1/2018+.
  • Rationale: EOD Primary Tumor is used to calculate Derived EOD 2018 T [785] (when applicable) and Derived Summary Stage 2018 [762]. Derivation will occur at the level of the central registry.
  • Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
  • Codes (In addition to schema-specific codes where needed)
    • 000 In situ, intraepithelial, noninvasive
    • 800 No evidence of primary tumor
    • 999 Unknown; primary tumor not stated Primary tumor cannot be assessed Not documented in patient record Death certificate only (DCO)
  • Codes
    • 100 Incidental histologic finding (for example, on TURP) in 5 percent or less of tissue resected (clinically inapparent)
    • 120 Tumor identified by needle biopsy (clinically inapparent/not palpable)
    • 150 Incidental histologic finding (for example, on TURP), number of foci or percent of involved tissue not specified (clinically inapparent/not palpable)
    • 200 Involves one-half of one side or less
    • 220 Involves both lobes/sides
    • 300 Localized, NOS Not known if clinically apparent or inapparent
    • 350 Bladder neck, microscopic invasion
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#772


All data

st_css() #IMPORTANT!
  eodprimarytumor <- as.factor(trimws(d[,"eodprimarytumor"]))
  levels(eodprimarytumor)[levels(eodprimarytumor)=="100"] <- "5_percent_or_less_tissue.100"
  levels(eodprimarytumor)[levels(eodprimarytumor)=="120"] <- "Identified_by_needle_biopsy.120"
  levels(eodprimarytumor)[levels(eodprimarytumor)=="150"] <- "number_of_foci_not_specified.150"
  levels(eodprimarytumor)[levels(eodprimarytumor)=="200"] <- "one_half.200"
  levels(eodprimarytumor)[levels(eodprimarytumor)=="220"] <- "both_lobes_sides.220"
  levels(eodprimarytumor)[levels(eodprimarytumor)=="300"] <- "Localized_NOS.300"
  levels(eodprimarytumor)[levels(eodprimarytumor)=="350"] <- "Bladder_neck_microscopic_invasion.350"

  new.d <- data.frame(new.d, eodprimarytumor)
  new.d <- apply_labels(new.d, eodprimarytumor = "eod_primary_tumor")

temp.d <- data.frame (new.d.1, eodprimarytumor)
summarytools::view(dfSummary(new.d$eodprimarytumor, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 eodprimarytumor [labelled, factor] eod_primary_tumor 1. 5_percent_or_less_tissue. 2. Identified_by_needle_biop 3. number_of_foci_not_specif 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_
2(3.1%)
35(54.7%)
1(1.6%)
9(14.1%)
1(1.6%)
2(3.1%)
12(18.8%)
2(3.1%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue.100 2. Identified_by_needle_biopsy.120 3. number_of_foci_not_specified.150 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_invasion.350 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue.100 2. Identified_by_needle_biopsy.120 3. number_of_foci_not_specified.150 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_invasion.350 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue.100 2. Identified_by_needle_biopsy.120 3. number_of_foci_not_specified.150 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_invasion.350 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue. 2. Identified_by_needle_biop 3. number_of_foci_not_specif 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_
2(3.3%)
33(54.1%)
1(1.6%)
8(13.1%)
1(1.6%)
2(3.3%)
12(19.7%)
2(3.3%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue. 2. Identified_by_needle_biop 3. number_of_foci_not_specif 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue. 2. Identified_by_needle_biop 3. number_of_foci_not_specif 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_
0(0.0%)
1(50.0%)
0(0.0%)
1(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_primary_tumor [factor] 1. 5_percent_or_less_tissue.100 2. Identified_by_needle_biopsy.120 3. number_of_foci_not_specified.150 4. one_half.200 5. 210 6. both_lobes_sides.220 7. Localized_NOS.300 8. Bladder_neck_microscopic_invasion.350 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD REGIONAL NODES

  • Description: EOD Regional Nodes is part of the EOD 2018 data collection system and is used to classify the regional lymph nodes involved with cancer at the time of diagnosis. See also EOD Primary Tumor [772] and EOD Mets [776]. Effective for cases diagnosed 1/1/2018+.
  • Rationale: EOD Regional Nodes is used to calculate Derived EOD 2018 N [815] (when applicable) and Derived Summary Stage 2018 [762]. Derivation will occur at the level of the central registry. Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
  • Codes
    • 000 None
    • 300 Hypogastric/Iliac, NOS/Pelvic, NOS/Pelvic, NOS/Sacral, NOS
    • 800 Regional lymph node(s), NOS Lymph node(s), NOS
    • 888 Not applicable–e.g., CNS, hematopoietic
    • 999 Unknown
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#774


All data

st_css() #IMPORTANT!
  eodregionalnodes <- as.factor(trimws(d[,"eodregionalnodes"]))
  levels(eodregionalnodes)[levels(eodregionalnodes)=="000"] <- "None.100"
  levels(eodregionalnodes)[levels(eodregionalnodes)=="300"] <- "Hypogastric.300"

  new.d <- data.frame(new.d, eodregionalnodes)
  new.d <- apply_labels(new.d, eodregionalnodes = "eod_regional_nodes")

temp.d <- data.frame (new.d.1, eodregionalnodes)
summarytools::view(dfSummary(new.d$eodregionalnodes, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 eodregionalnodes [labelled, factor] eod_regional_nodes 1. None.100 2. Hypogastric.300
63(98.4%)
1(1.6%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300
60(98.4%)
1(1.6%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300
1(100.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300
2(100.0%)
0(0.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_regional_nodes [factor] 1. None.100 2. Hypogastric.300 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD METS

  • Description: EOD Mets is part of the EOD 2018 data collection system and is used to classify the distant site(s) of metastatic involvement at time of diagnosis. See also EOD Primary Tumor [772] and EOD Regional Nodes [774]. Effective for cases diagnosed 1/1/2018+.
  • Rationale: EOD Mets is used to calculate Derived EOD 2018 M [795] (when applicable) and Derived Summary Stage 2018 [762]. Derivation will occur at the level of the central registry.
  • Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
  • Codes
    • 00 None No distant metastasis Unknown if distant metastasis
    • 30 Bone WITH or WITHOUT distant lymph node(s)
    • 50 Other specified distant metastasis/WITH or WITHOUT distant lymph node(s) or bone metastasis/Carcinomatosis
    • 88 Not applicable: Information not collected for this schema Use for these sites only: HemeRetic; Ill Defined Other (includes unknown primary site); Kaposi Sarcoma; Lymphoma; Lymphoma-CLL/SLL; Myeloma Plasma Cell Disorder
    • 99 Death certificate only (DCO)
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#776


All data

st_css() #IMPORTANT!
  eodmets <- as.factor(trimws(d[,"eodmets"]))
  levels(eodmets)[levels(eodmets)=="00"] <- "None.100"
  levels(eodmets)[levels(eodmets)=="30"] <- "Bone_WITH_or_WITHOUT.30"
  levels(eodmets)[levels(eodmets)=="50"] <- "Other_specified_metastasis.50"

  new.d <- data.frame(new.d, eodmets)
  new.d <- apply_labels(new.d, eodmets = "eod_mets")
temp.d <- data.frame (new.d.1, eodmets)
summarytools::view(dfSummary(new.d$eodmets), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 eodmets [labelled, factor] eod_mets 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasi
60(93.8%)
3(4.7%)
1(1.6%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasis.50 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasis.50 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasis.50 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasi
57(93.4%)
3(4.9%)
1(1.6%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasi
1(100.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasi
2(100.0%)
0(0.0%)
0(0.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 eod_mets [factor] 1. None.100 2. Bone_WITH_or_WITHOUT.30 3. Other_specified_metastasis.50 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD–EXTENSION

  • Description: Part of the 10-digit EOD [779]. Detailed site-specific codes for anatomic EOD used by SEER for tumors diagnosed from January 1, 1988, through December 31, 2003.

  • Codes were revised effective January 1, 1998, to reflect changes in the AJCC Cancer Staging Manual, Fifth Edition.

  • Rationale: Site-specific EOD codes provide extensive detail describing disease extent. The EOD codes can be grouped into different stage categories for analysis (e.g., historical summary stage categories consistent with those used in published SEER data since 1973, or more recently, AJCC stage groupings). The codes are updated as needed, but updates are usually backward compatible with old categories. See Comparative Staging Guide for Cancer6.

  • Codes (See SEER Extent of Disease, 1988: Codes and Coding Instructions, Third Edition8 for site-specific codes and coding rules for all EOD fields.)

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#790


All data

st_css() #IMPORTANT!
  eodextension <- as.factor(trimws(d[,"eodextension"]))

  new.d <- data.frame(new.d, eodextension)
  new.d <- apply_labels(new.d, eodextension = "eod_extension")
temp.d <- data.frame (new.d.1, eodextension)
summarytools::view(dfSummary(new.d$eodextension), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 eodextension [labelled, factor] eod_extension No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD–EXTENSION PROST PATH

  • Description: Part of the 10-digit EOD [779]. Detailed site-specific codes for anatomic EOD used by SEER for tumors diagnosed from January 1, 1988, through December 31, 2003.

  • Codes were revised effective January 1, 1998, to reflect changes in the AJCC Cancer Staging Manual, Fifth Edition.

  • Rationale: Site-specific EOD codes provide extensive detail describing disease extent. The EOD codes can be grouped into different stage categories for analysis (e.g., historical summary stage categories consistent with those used in published SEER data since 1973, or more recently, AJCC stage groupings). The codes are updated as needed, but updates are usually backward compatible with old categories. See Comparative Staging Guide for Cancer.

  • EOD–Extension Prost Path is an additional field for prostate cancer only to reflect information from radical prostatectomy, effective for January 1, 1995, through December 31, 2003, diagnoses. The field is left blank for all other primaries.

  • Codes (See SEER Extent of Disease, 1988: Codes and Coding Instructions, Third Edition8 for site-specific codes and coding rules for all EOD fields.)

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#800


All data

st_css() #IMPORTANT!
  eodextensionprostpath <- as.factor(trimws(d[,"eodextensionprostpath"]))

  new.d <- data.frame(new.d, eodextensionprostpath)
  new.d <- apply_labels(new.d, eodextensionprostpath = "eod_extension_prost_path")
temp.d <- data.frame (new.d.1, eodextensionprostpath)
summarytools::view(dfSummary(new.d$eodextensionprostpath), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 eodextensionprostpath [labelled, factor] eod_extension_prost_path No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD–LYMPH NODE INVOLV

  • Description: Part of the 10-digit EOD [779]. Detailed site-specific codes for anatomic EOD used by SEER for tumors diagnosed from January 1, 1988, through December 31, 2003.

  • Codes were revised effective January 1, 1998, to reflect changes in the AJCC Cancer Staging Manual, Fifth Edition.

  • Rationale: Site-specific EOD codes provide extensive detail describing disease extent. The EOD codes can be grouped into different stage categories for analysis (e.g., historical summary stage categories consistent with those used in published SEER data since 1973, or more recently, AJCC stage groupings). The codes are updated as needed, but updates are usually backward compatible with old categories. See Comparative Staging Guide for Cancer.

  • Codes (See SEER Extent of Disease, 1988: Codes and Coding Instructions, Third Edition8 for site-specific codes and coding rules for all EOD fields.)

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#810


All data

st_css() #IMPORTANT!
  eodlymphnodeinvolv <- as.factor(trimws(d[,"eodlymphnodeinvolv"]))

  new.d <- data.frame(new.d, eodlymphnodeinvolv)
  new.d <- apply_labels(new.d, eodlymphnodeinvolv = "eod_lymph_node_involv")
temp.d <- data.frame (new.d.1, eodlymphnodeinvolv)
summarytools::view(dfSummary(new.d$eodlymphnodeinvolv), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 eodlymphnodeinvolv [labelled, factor] eod_lymph_node_involv No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED EOD 2018 STAGE GROUP

  • Description: Derived EOD 2018 Stage Group is derived using the EOD data collection system (EOD Primary Tumor [772], EOD Regional Nodes [774] and EOD Mets [776]) algorithm. Other data items may be included in the derivation process. Effective for cases diagnosed 1/1/2018+.

  • Rationale: Derived EOD 2018 Stage Group can be used to evaluate disease spread at diagnosis, treatment patterns and outcomes over time.

  • Derived EOD 2018 Stage group is only available at the central registry level.

  • Codes: See the most current version of EOD (https://staging.seer.cancer.gov/) for rules and site-specific codes and coding structures.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#818


All data

  derivedeod2018stagegroup <- as.factor(trimws(d[,"derivedeod2018stagegroup"]))
  # THIS CODING NEEDS TO BE CONFIRMED
  levels(derivedeod2018stagegroup) <- list(T.1="1",
                                          T.1B="1B",
                                          T.2A="2A",
                                          T.2B="2B",
                                          T.2C="2C",
                                          T.3="3",
                                          T.3A="3A",
                                          T.3B="3B",
                                          T.3C="3C",
                                          T.4="4",
                                          T.4A="4A",
                                          T.4B="4B",
                                          Do_Not_Know.88="88",
                                          Unknown.99="99")
    derivedeod2018stagegroup <- relevel(derivedeod2018stagegroup, ref="T.1")
    new.d <- data.frame(new.d, derivedeod2018stagegroup)
    new.d <- apply_labels(new.d, derivedeod2018stagegroup = "Tumor Stage Group")
    #summary(new.d$derivedeod2018stagegroup)
temp.d <- data.frame (new.d.1, derivedeod2018stagegroup)
summarytools::view(dfSummary(new.d$derivedeod2018stagegroup, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedeod2018stagegroup [labelled, factor] Tumor Stage Group 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99
7(10.9%)
1(1.6%)
2(3.1%)
16(25.0%)
18(28.1%)
0(0.0%)
3(4.7%)
5(7.8%)
4(6.2%)
0(0.0%)
0(0.0%)
4(6.2%)
1(1.6%)
3(4.7%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99
6(9.8%)
0(0.0%)
2(3.3%)
15(24.6%)
18(29.5%)
0(0.0%)
3(4.9%)
5(8.2%)
4(6.6%)
0(0.0%)
0(0.0%)
4(6.6%)
1(1.6%)
3(4.9%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99
1(50.0%)
1(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_eod_2018_stage_group [factor] 1. T.1 2. T.1B 3. T.2A 4. T.2B 5. T.2C 6. T.3 7. T.3A 8. T.3B 9. T.3C 10. T.4 11. T.4A 12. T.4B 13. Do_Not_Know.88 14. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


REGIONAL NODES POSITIVE

  • Description: Records the exact number of regional nodes examined by the pathologist and found to contain metastases. Beginning with tumors diagnosed on or after January 1, 2004, this item is a component of the Collaborative Stage system. For tumors diagnosed from 1988 through 2003, this item was part of the 10-digit EOD [779], detailed site-specific codes for anatomic EOD.
  • Rationale: This data item is necessary for pathologic staging, and it serves as a quality measure for pathology reports and the extent of the surgical evaluation and treatment of the patient.
  • Codes
    • 00 All nodes examined are negative
    • 01-89 1-89 nodes are positive (code exact number of nodes positive)
    • 90 90 or more nodes are positive
    • 95 Positive aspiration of lymph node(s) was performed
    • 97 Positive nodes are documented, but the number is unspecified
    • 98 No nodes were examined
    • 99 It is unknown whether nodes are positive; not applicable; not stated in patient record
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#820


All data

st_css() #IMPORTANT!
  regionalnodespositive <- as.factor(trimws(d[,"regionalnodespositive"]))
  levels(regionalnodespositive)[levels(regionalnodespositive)=="0"] <- "All_negative.0"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="1"] <- "1_node_posi.1"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="2"] <- "2_nodes_posi.2"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="3"] <- "3_nodes_posi.3"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="4"] <- "4_nodes_posi.4"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="5"] <- "5_nodes_posi.5"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="6"] <- "6_nodes_posi.6"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="18"] <- "18_nodes_posi.18"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="19"] <- "19_nodes_posi.19"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="95"] <- "Positive_aspiration.95"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="98"] <- "No_nodes_examined.98"
  levels(regionalnodespositive)[levels(regionalnodespositive)=="99"] <- "Unknown.99"

  new.d <- data.frame(new.d, regionalnodespositive)
  new.d <- apply_labels(new.d, regionalnodespositive = "regional_nodes_positive")

temp.d <- data.frame (new.d.1, regionalnodespositive)
summarytools::view(dfSummary(new.d$regionalnodespositive, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 regionalnodespositive [labelled, factor] regional_nodes_positive 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
733(20.6%)
140(3.9%)
3(0.1%)
1(0.0%)
2(0.1%)
1(0.0%)
34(1.0%)
1(0.0%)
1(0.0%)
16(0.4%)
6(0.2%)
4(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
6(0.2%)
2547(71.6%)
58(1.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
85(26.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(1.9%)
1(0.3%)
0(0.0%)
3(0.9%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
225(70.1%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
43(20.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
162(77.1%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
114(36.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.3%)
0(0.0%)
0(0.0%)
6(1.9%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
2(0.6%)
183(58.1%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
62(17.4%)
45(12.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
3(0.8%)
0(0.0%)
0(0.0%)
2(0.6%)
1(0.3%)
2(0.6%)
1(0.3%)
0(0.0%)
0(0.0%)
1(0.3%)
232(65.2%)
3(0.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
162(27.7%)
68(11.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(0.9%)
0(0.0%)
0(0.0%)
2(0.3%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
339(57.9%)
8(1.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
262(14.9%)
27(1.5%)
2(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
14(0.8%)
0(0.0%)
1(0.1%)
3(0.2%)
2(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
0(0.0%)
3(0.2%)
1395(79.5%)
41(2.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_positive [factor] 1. All_negative.0 2. 00 3. 01 4. 02 5. 03 6. 09 7. 1_node_posi.1 8. 18_nodes_posi.18 9. 19_nodes_posi.19 10. 2_nodes_posi.2 11. 3_nodes_posi.3 12. 4_nodes_posi.4 13. 5_nodes_posi.5 14. 6_nodes_posi.6 15. 8 16. Positive_aspiration.95 17. No_nodes_examined.98 18. Unknown.99
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
11(68.8%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


REGIONAL NODES EXAMINED

  • Description: Records the total number of regional lymph nodes that were removed and examined by the pathologist. Beginning with tumors diagnosed on or after January 1, 2004, this item is a component of the Collaborative Stage system.
  • Rationale: This data item serves as a quality measure of the pathologic and surgical evaluation and treatment of the patient.
  • Codes
    • 00 No nodes were examined
    • 01-89 1-89 nodes were examined (code the exact number of regional lymph nodes examined)
    • 90 90 or more nodes were examined
    • 95 No regional nodes were removed, but aspiration of regional nodes was performed
    • 96 Regional lymph node removal was documented as a sampling, and the number of nodes is unknown/not stated
    • 97 Regional lymph node removal was documented as a dissection, and the number of nodes is unknown/not stated
    • 98 Regional lymph nodes were surgically removed, but the number of lymph nodes is unknown/not stated and not documented as a sampling or dissection; nodes were examined, but the number is unknown
    • 99 It is unknown whether nodes were examined; not stated in patient record
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#830


All data

st_css() #IMPORTANT!
  regionalnodesexamined <- as.factor(trimws(d[,"regionalnodesexamined"]))
  levels(regionalnodesexamined)[levels(regionalnodesexamined)=="0"] <- "No_nodes_examined.0"
  levels(regionalnodesexamined)[levels(regionalnodesexamined)=="95"] <- "No_removed_aspiration_performed.95"
  levels(regionalnodesexamined)[levels(regionalnodesexamined)=="97"] <- "dissection_number_unknown.97"
  levels(regionalnodesexamined)[levels(regionalnodesexamined)=="98"] <- "nodes_removed_number_unknown.98"
  levels(regionalnodesexamined)[levels(regionalnodesexamined)=="99"] <- "Unknown.99"

  new.d <- data.frame(new.d, regionalnodesexamined)
  new.d <- apply_labels(new.d, regionalnodesexamined = "regional_nodes_examined")

temp.d <- data.frame (new.d.1, regionalnodesexamined)
summarytools::view(dfSummary(new.d$regionalnodesexamined, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 regionalnodesexamined [labelled, factor] regional_nodes_examined 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
2083(58.6%)
464(13.0%)
6(0.2%)
13(0.4%)
15(0.4%)
14(0.4%)
8(0.2%)
8(0.2%)
12(0.3%)
11(0.3%)
7(0.2%)
35(1.0%)
49(1.4%)
40(1.1%)
30(0.8%)
27(0.8%)
25(0.7%)
14(0.4%)
17(0.5%)
6(0.2%)
11(0.3%)
11(0.3%)
100(2.8%)
13(0.4%)
6(0.2%)
8(0.2%)
7(0.2%)
4(0.1%)
4(0.1%)
2(0.1%)
4(0.1%)
6(0.2%)
74(2.1%)
2(0.1%)
3(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
2(0.1%)
1(0.0%)
2(0.1%)
66(1.9%)
1(0.0%)
1(0.0%)
1(0.0%)
83(2.3%)
1(0.0%)
1(0.0%)
1(0.0%)
45(1.3%)
53(1.5%)
46(1.3%)
36(1.0%)
7(0.2%)
1(0.0%)
5(0.1%)
5(0.1%)
56(1.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
225(70.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
4(1.2%)
9(2.8%)
3(0.9%)
1(0.3%)
4(1.2%)
1(0.3%)
2(0.6%)
0(0.0%)
2(0.6%)
2(0.6%)
5(1.6%)
3(0.9%)
0(0.0%)
2(0.6%)
2(0.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
0(0.0%)
7(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
12(3.7%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.2%)
11(3.4%)
4(1.2%)
5(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
162(77.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
3(1.4%)
2(1.0%)
3(1.4%)
0(0.0%)
2(1.0%)
0(0.0%)
1(0.5%)
0(0.0%)
2(1.0%)
0(0.0%)
7(3.3%)
0(0.0%)
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
6(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
3(1.4%)
0(0.0%)
0(0.0%)
0(0.0%)
3(1.4%)
2(1.0%)
4(1.9%)
1(0.5%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
183(58.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.3%)
7(2.2%)
6(1.9%)
5(1.6%)
5(1.6%)
5(1.6%)
3(1.0%)
2(0.6%)
2(0.6%)
3(1.0%)
4(1.3%)
13(4.1%)
4(1.3%)
2(0.6%)
2(0.6%)
1(0.3%)
0(0.0%)
2(0.6%)
0(0.0%)
1(0.3%)
0(0.0%)
14(4.4%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
10(3.2%)
0(0.0%)
0(0.0%)
1(0.3%)
10(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
5(1.6%)
4(1.3%)
2(0.6%)
6(1.9%)
2(0.6%)
0(0.0%)
1(0.3%)
0(0.0%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
101(28.4%)
131(36.8%)
3(0.8%)
5(1.4%)
2(0.6%)
3(0.8%)
3(0.8%)
2(0.6%)
4(1.1%)
3(0.8%)
1(0.3%)
1(0.3%)
8(2.2%)
5(1.4%)
5(1.4%)
7(2.0%)
2(0.6%)
4(1.1%)
3(0.8%)
0(0.0%)
1(0.3%)
2(0.6%)
4(1.1%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
5(1.4%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(1.4%)
0(0.0%)
0(0.0%)
0(0.0%)
11(3.1%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
4(1.1%)
5(1.4%)
7(2.0%)
1(0.3%)
1(0.3%)
2(0.6%)
1(0.3%)
3(0.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
125(21.4%)
214(36.6%)
1(0.2%)
7(1.2%)
8(1.4%)
10(1.7%)
2(0.3%)
4(0.7%)
7(1.2%)
6(1.0%)
4(0.7%)
8(1.4%)
10(1.7%)
7(1.2%)
4(0.7%)
6(1.0%)
4(0.7%)
3(0.5%)
0(0.0%)
2(0.3%)
1(0.2%)
1(0.2%)
31(5.3%)
3(0.5%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
24(4.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
20(3.4%)
0(0.0%)
0(0.0%)
0(0.0%)
18(3.1%)
0(0.0%)
0(0.0%)
0(0.0%)
14(2.4%)
11(1.9%)
13(2.2%)
4(0.7%)
0(0.0%)
0(0.0%)
1(0.2%)
2(0.3%)
7(1.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
1276(72.7%)
119(6.8%)
2(0.1%)
1(0.1%)
5(0.3%)
1(0.1%)
3(0.2%)
2(0.1%)
1(0.1%)
2(0.1%)
2(0.1%)
18(1.0%)
17(1.0%)
11(0.6%)
10(0.6%)
7(0.4%)
8(0.5%)
3(0.2%)
9(0.5%)
2(0.1%)
2(0.1%)
2(0.1%)
39(2.2%)
2(0.1%)
2(0.1%)
1(0.1%)
3(0.2%)
2(0.1%)
1(0.1%)
0(0.0%)
0(0.0%)
6(0.3%)
22(1.3%)
1(0.1%)
1(0.1%)
2(0.1%)
0(0.0%)
0(0.0%)
2(0.1%)
1(0.1%)
1(0.1%)
18(1.0%)
1(0.1%)
1(0.1%)
0(0.0%)
28(1.6%)
1(0.1%)
1(0.1%)
1(0.1%)
17(1.0%)
21(1.2%)
17(1.0%)
13(0.7%)
4(0.2%)
0(0.0%)
0(0.0%)
2(0.1%)
40(2.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 regional_nodes_examined [factor] 1. No_nodes_examined.0 2. 00 3. 01 4. 02 5. 03 6. 04 7. 05 8. 06 9. 07 10. 08 11. 09 12. 1 13. 10 14. 11 15. 12 16. 13 17. 14 18. 15 19. 16 20. 17 21. 18 22. 19 23. 2 24. 20 25. 21 26. 22 27. 23 28. 24 29. 25 30. 26 31. 27 32. 29 33. 3 34. 30 35. 32 36. 33 37. 34 38. 35 39. 36 40. 37 41. 39 42. 4 43. 40 44. 42 45. 46 46. 5 47. 50 48. 51 49. 52 50. 6 51. 7 52. 8 53. 9 54. No_removed_aspiration_per 55. 96 56. dissection_number_unknown 57. nodes_removed_number_unkn 58. Unknown.99
11(68.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM PATH T

  • Description: Detailed site-specific codes for the pathologic tumor (T) as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#880


All data

st_css() #IMPORTANT!
  tnmpatht <- as.factor(trimws(d[,"tnmpatht"]))
  levels(tnmpatht)[levels(tnmpatht)=="88"] <- "Not_applicable.88"

  new.d <- data.frame(new.d, tnmpatht)
  new.d <- apply_labels(new.d, tnmpatht = "tnm_path_t")

temp.d <- data.frame (new.d.1, tnmpatht)
summarytools::view(dfSummary(new.d$tnmpatht, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmpatht [labelled, factor] tnm_path_t 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
1(0.1%)
1(0.1%)
21(1.4%)
11(0.7%)
2(0.1%)
147(10.0%)
26(1.8%)
22(1.5%)
1(0.1%)
1(0.1%)
1(0.1%)
35(2.4%)
41(2.8%)
10(0.7%)
514(34.9%)
3(0.2%)
180(12.2%)
100(6.8%)
2(0.1%)
242(16.4%)
111(7.5%)
 2085 (58.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(2.7%)
6(5.4%)
2(1.8%)
63(56.2%)
0(0.0%)
18(16.1%)
11(9.8%)
0(0.0%)
9(8.0%)
0(0.0%)
 209 (65.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.4%)
0(0.0%)
0(0.0%)
5(7.0%)
0(0.0%)
19(26.8%)
1(1.4%)
17(23.9%)
5(7.0%)
0(0.0%)
23(32.4%)
0(0.0%)
 139 (66.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.5%)
5(3.8%)
0(0.0%)
74(56.1%)
1(0.8%)
26(19.7%)
7(5.3%)
0(0.0%)
17(12.9%)
0(0.0%)
 183 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
0(0.0%)
0(0.0%)
1(0.7%)
2(1.5%)
1(0.7%)
32(23.4%)
5(3.6%)
3(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.5%)
2(1.5%)
1(0.7%)
39(28.5%)
0(0.0%)
18(13.1%)
15(10.9%)
0(0.0%)
1(0.7%)
15(10.9%)
 219 (61.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
0(0.0%)
0(0.0%)
1(0.3%)
4(1.4%)
1(0.3%)
37(12.5%)
10(3.4%)
6(2.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(1.0%)
8(2.7%)
2(0.7%)
116(39.2%)
1(0.3%)
44(14.9%)
22(7.4%)
1(0.3%)
30(10.1%)
10(3.4%)
 289 (49.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
1(0.1%)
1(0.1%)
19(2.7%)
5(0.7%)
0(0.0%)
74(10.3%)
10(1.4%)
13(1.8%)
1(0.1%)
0(0.0%)
1(0.1%)
25(3.5%)
15(2.1%)
5(0.7%)
203(28.4%)
0(0.0%)
57(8.0%)
40(5.6%)
1(0.1%)
162(22.6%)
83(11.6%)
 1038 (59.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_t [factor] 1. 1B 2. 1C 3. 2 4. 2A 5. 2B 6. 2C 7. 3A 8. 3B 9. Not_applicable.88 10. p0 11. p1A 12. p2 13. p2A 14. p2B 15. p2C 16. p3 17. p3A 18. p3B 19. p4 20. pX 21. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(50.0%)
1(12.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(37.5%)
 8 (50.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM PATH N

  • Description: Detailed site-specific codes for the pathologic nodes (N) as defined by AJCC and recorded by physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#890


All data

st_css() #IMPORTANT!
  tnmpathn <- as.factor(trimws(d[,"tnmpathn"]))
  levels(tnmpathn)[levels(tnmpathn)=="88"] <- "Not_applicable.88"

  new.d <- data.frame(new.d, tnmpathn)
  new.d <- apply_labels(new.d, tnmpathn = "tnm_path_n")

temp.d <- data.frame (new.d.1, tnmpathn)
summarytools::view(dfSummary(new.d$tnmpathn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmpathn [labelled, factor] tnm_path_n 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
178(14.0%)
10(0.8%)
1(0.1%)
118(9.3%)
453(35.7%)
43(3.4%)
339(26.7%)
126(9.9%)
 2289 (64.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
0(0.0%)
0(0.0%)
0(0.0%)
3(2.7%)
60(53.1%)
9(8.0%)
41(36.3%)
0(0.0%)
 208 (64.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
0(0.0%)
0(0.0%)
0(0.0%)
4(5.6%)
39(54.9%)
4(5.6%)
24(33.8%)
0(0.0%)
 139 (66.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
0(0.0%)
0(0.0%)
0(0.0%)
1(0.8%)
85(65.9%)
7(5.4%)
36(27.9%)
0(0.0%)
 186 (59.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
37(41.6%)
2(2.2%)
0(0.0%)
4(4.5%)
19(21.3%)
3(3.4%)
6(6.7%)
18(20.2%)
 267 (75.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
52(32.9%)
3(1.9%)
0(0.0%)
5(3.2%)
65(41.1%)
0(0.0%)
32(20.3%)
1(0.6%)
 427 (73.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
84(11.9%)
5(0.7%)
1(0.1%)
101(14.4%)
185(26.3%)
20(2.8%)
200(28.4%)
107(15.2%)
 1051 (59.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_n [factor] 1. 0 2. 1 3. Not_applicable.88 4. c0 5. p0 6. p1 7. pX 8. X
5(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 11 (68.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM PATH M

  • Description: Detailed site-specific codes for the pathologic metastases (M) as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#900


All data

st_css() #IMPORTANT!
  tnmpathm <- as.factor(trimws(d[,"tnmpathm"]))
  levels(tnmpathm)[levels(tnmpathm)=="88"] <- "Not_applicable.88"

  new.d <- data.frame(new.d, tnmpathm)
  new.d <- apply_labels(new.d, tnmpathm = "tnm_path_m")

temp.d <- data.frame (new.d.1, tnmpathm)
summarytools::view(dfSummary(new.d$tnmpathm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmpathm [labelled, factor] tnm_path_m 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
1(0.1%)
1(0.1%)
1(0.1%)
959(98.2%)
2(0.2%)
1(0.1%)
4(0.4%)
1(0.1%)
2(0.2%)
2(0.2%)
3(0.3%)
 2580 (72.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
0(0.0%)
0(0.0%)
0(0.0%)
89(96.7%)
1(1.1%)
0(0.0%)
1(1.1%)
0(0.0%)
1(1.1%)
0(0.0%)
0(0.0%)
 229 (71.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
0(0.0%)
0(0.0%)
0(0.0%)
44(97.8%)
1(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 165 (78.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
0(0.0%)
0(0.0%)
0(0.0%)
96(98.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(2.0%)
 217 (68.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
1(1.3%)
1(1.3%)
0(0.0%)
72(96.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.3%)
0(0.0%)
 281 (78.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
0(0.0%)
0(0.0%)
0(0.0%)
222(98.7%)
0(0.0%)
0(0.0%)
2(0.9%)
1(0.4%)
0(0.0%)
0(0.0%)
0(0.0%)
 360 (61.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C
0(0.0%)
0(0.0%)
1(0.2%)
436(98.6%)
0(0.0%)
1(0.2%)
1(0.2%)
0(0.0%)
1(0.2%)
1(0.2%)
1(0.2%)
 1312 (74.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_m [factor] 1. 0 2. 1B 3. Not_applicable.88 4. c0 5. c1 6. c1A 7. c1B 8. p1 9. p1A 10. p1B 11. p1C All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM PATH STAGE GROUP

  • Description: Detailed site-specific codes for the pathologic stage group as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • 99 Unknown, unstaged

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#910


All data

st_css() #IMPORTANT!
  tnmpathstagegroup <- as.factor(trimws(d[,"tnmpathstagegroup"]))
  levels(tnmpathstagegroup)[levels(tnmpathstagegroup)=="99"] <- "Unknown.99"

  new.d <- data.frame(new.d, tnmpathstagegroup)
  new.d <- apply_labels(new.d, tnmpathstagegroup = "tnm_path_stage_group")

temp.d <- data.frame (new.d.1, tnmpathstagegroup)
summarytools::view(dfSummary(new.d$tnmpathstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmpathstagegroup [labelled, factor] tnm_path_stage_group 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
14(0.6%)
6(0.2%)
14(0.6%)
123(4.9%)
250(10.0%)
80(3.2%)
2013(80.5%)
 1057 (29.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
16(7.5%)
12(5.6%)
184(86.0%)
 107 (33.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
18(13.0%)
5(3.6%)
115(83.3%)
 72 (34.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
3(1.8%)
0(0.0%)
0(0.0%)
0(0.0%)
25(15.2%)
9(5.5%)
128(77.6%)
 150 (47.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
0(0.0%)
0(0.0%)
6(2.4%)
60(24.5%)
28(11.4%)
14(5.7%)
137(55.9%)
 111 (31.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
2(0.4%)
3(0.7%)
5(1.1%)
46(10.1%)
74(16.2%)
11(2.4%)
315(69.1%)
 129 (22.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
8(0.6%)
2(0.2%)
3(0.2%)
13(1.0%)
88(6.9%)
29(2.3%)
1130(88.8%)
 481 (27.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_path_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
0(0.0%)
0(0.0%)
0(0.0%)
4(44.4%)
1(11.1%)
0(0.0%)
4(44.4%)
 7 (43.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM CLIN T

  • Description: Detailed site-specific codes for the clinical tumor (T) as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#940


All data

st_css() #IMPORTANT!
  tnmclint <- as.factor(trimws(d[,"tnmclint"]))
  levels(tnmclint)[levels(tnmclint)=="88"] <- "Not_applicable.88"

  new.d <- data.frame(new.d, tnmclint)
  new.d <- apply_labels(new.d, tnmclint = "tnm_clin_t")

temp.d <- data.frame (new.d.1, tnmclint)
summarytools::view(dfSummary(new.d$tnmclint, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmclint [labelled, factor] tnm_clin_t 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
1(0.0%)
1(0.0%)
5(0.2%)
2(0.1%)
437(14.3%)
29(0.9%)
22(0.7%)
10(0.3%)
23(0.8%)
2(0.1%)
4(0.1%)
5(0.2%)
5(0.2%)
39(1.3%)
18(0.6%)
7(0.2%)
1854(60.6%)
104(3.4%)
145(4.7%)
65(2.1%)
120(3.9%)
10(0.3%)
23(0.8%)
28(0.9%)
11(0.4%)
68(2.2%)
22(0.7%)
 497 (14.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.6%)
2(0.8%)
0(0.0%)
173(70.0%)
10(4.0%)
21(8.5%)
7(2.8%)
18(7.3%)
1(0.4%)
0(0.0%)
3(1.2%)
3(1.2%)
5(2.0%)
0(0.0%)
 74 (23.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.6%)
1(0.6%)
122(69.7%)
2(1.1%)
22(12.6%)
6(3.4%)
16(9.1%)
1(0.6%)
1(0.6%)
0(0.0%)
1(0.6%)
2(1.1%)
0(0.0%)
 35 (16.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.8%)
3(1.3%)
1(0.4%)
158(66.7%)
12(5.1%)
20(8.4%)
6(2.5%)
20(8.4%)
1(0.4%)
2(0.8%)
4(1.7%)
2(0.8%)
6(2.5%)
0(0.0%)
 78 (24.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
0(0.0%)
0(0.0%)
2(0.7%)
0(0.0%)
64(23.8%)
7(2.6%)
4(1.5%)
2(0.7%)
2(0.7%)
0(0.0%)
1(0.4%)
0(0.0%)
0(0.0%)
3(1.1%)
1(0.4%)
1(0.4%)
115(42.8%)
17(6.3%)
11(4.1%)
13(4.8%)
10(3.7%)
0(0.0%)
2(0.7%)
2(0.7%)
1(0.4%)
6(2.2%)
5(1.9%)
 87 (24.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
0(0.0%)
1(0.2%)
1(0.2%)
0(0.0%)
76(13.3%)
11(1.9%)
3(0.5%)
2(0.4%)
3(0.5%)
0(0.0%)
1(0.2%)
0(0.0%)
1(0.2%)
7(1.2%)
4(0.7%)
3(0.5%)
340(59.6%)
35(6.1%)
26(4.6%)
10(1.8%)
16(2.8%)
1(0.2%)
8(1.4%)
6(1.1%)
2(0.4%)
11(1.9%)
2(0.4%)
 15 (2.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
1(0.1%)
0(0.0%)
2(0.1%)
2(0.1%)
289(18.6%)
11(0.7%)
15(1.0%)
6(0.4%)
18(1.2%)
2(0.1%)
2(0.1%)
5(0.3%)
4(0.3%)
23(1.5%)
7(0.5%)
1(0.1%)
945(61.0%)
28(1.8%)
45(2.9%)
23(1.5%)
40(2.6%)
6(0.4%)
10(0.6%)
13(0.8%)
2(0.1%)
38(2.5%)
12(0.8%)
 204 (11.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_t [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. 2 7. 2A 8. 2B 9. 2C 10. 3 11. 3A 12. 3B 13. 4 14. c1 15. c1A 16. c1B 17. c1C 18. c2 19. c2A 20. c2B 21. c2C 22. c3 23. c3A 24. c3B 25. c4 26. cX 27. X
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
8(66.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(25.0%)
 4 (25.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM CLIN N

  • Description: Detailed site-specific codes for the clinical nodes (N) as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#950


All data

st_css() #IMPORTANT!
  tnmclinn <- as.factor(trimws(d[,"tnmclinn"]))
  levels(tnmclinn)[levels(tnmclinn)=="88"] <- "Not_applicable.88"

  new.d <- data.frame(new.d, tnmclinn)
  new.d <- apply_labels(new.d, tnmclinn = "tnm_clin_n")

temp.d <- data.frame (new.d.1, tnmclinn)
summarytools::view(dfSummary(new.d$tnmclinn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmclinn [labelled, factor] tnm_clin_n 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
528(19.3%)
10(0.4%)
2054(75.0%)
44(1.6%)
72(2.6%)
30(1.1%)
 819 (23.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
0(0.0%)
0(0.0%)
235(94.4%)
6(2.4%)
8(3.2%)
0(0.0%)
 72 (22.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
0(0.0%)
0(0.0%)
168(96.0%)
6(3.4%)
1(0.6%)
0(0.0%)
 35 (16.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
0(0.0%)
0(0.0%)
214(91.8%)
13(5.6%)
6(2.6%)
0(0.0%)
 82 (26.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
73(43.5%)
2(1.2%)
80(47.6%)
0(0.0%)
3(1.8%)
10(6.0%)
 188 (52.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
100(26.2%)
1(0.3%)
270(70.9%)
2(0.5%)
8(2.1%)
0(0.0%)
 204 (34.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
347(22.8%)
7(0.5%)
1087(71.3%)
17(1.1%)
46(3.0%)
20(1.3%)
 230 (13.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_n [factor] 1. 0 2. 1 3. c0 4. c1 5. cX 6. X
8(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 8 (50.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM CLIN M

  • Description: Detailed site-specific codes for the clinical metastases (M) as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#960


All data

st_css() #IMPORTANT!
  tnmclinm <- as.factor(trimws(d[,"tnmclinm"]))
  levels(tnmclinm)[levels(tnmclinm)=="88"] <- "Not_applicable.88"

  new.d <- data.frame(new.d, tnmclinm)
  new.d <- apply_labels(new.d, tnmclinm = "tnm_clin_m")

temp.d <- data.frame (new.d.1, tnmclinm)
summarytools::view(dfSummary(new.d$tnmclinm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmclinm [labelled, factor] tnm_clin_m 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
522(19.4%)
2(0.1%)
1(0.0%)
6(0.2%)
1(0.0%)
2(0.1%)
2107(78.2%)
10(0.4%)
3(0.1%)
31(1.2%)
5(0.2%)
1(0.0%)
1(0.0%)
2(0.1%)
 863 (24.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
238(96.4%)
1(0.4%)
1(0.4%)
7(2.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 74 (23.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
166(96.5%)
2(1.2%)
0(0.0%)
3(1.7%)
1(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
 38 (18.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
219(94.8%)
3(1.3%)
1(0.4%)
4(1.7%)
3(1.3%)
0(0.0%)
0(0.0%)
1(0.4%)
 84 (26.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
71(45.8%)
0(0.0%)
1(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
83(53.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 201 (56.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
93(24.9%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
275(73.5%)
0(0.0%)
0(0.0%)
4(1.1%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
 211 (36.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
350(23.2%)
1(0.1%)
0(0.0%)
6(0.4%)
1(0.1%)
1(0.1%)
1126(74.8%)
4(0.3%)
1(0.1%)
13(0.9%)
1(0.1%)
0(0.0%)
1(0.1%)
1(0.1%)
 248 (14.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_m [factor] 1. 0 2. 1 3. 1A 4. 1B 5. 1C 6. Not_applicable.88 7. c0 8. c1 9. c1A 10. c1B 11. c1C 12. p1 13. p1A 14. p1B
8(88.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(11.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 7 (43.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TNM CLIN STAGE GROUP

  • Description: Detailed site-specific codes for the clinical stage group as defined by AJCC and recorded by the physician.

  • Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.

  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.

  • Codes (in addition to those published in the AJCC Cancer Staging Manual)

    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • 99 Unknown, not staged

  • Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#970


All data

st_css() #IMPORTANT!
  tnmclinstagegroup <- as.factor(trimws(d[,"tnmclinstagegroup"]))
  levels(tnmclinstagegroup)[levels(tnmclinstagegroup)=="88"] <- "Not_applicable.88"
  levels(tnmclinstagegroup)[levels(tnmclinstagegroup)=="99"] <- "Unknown.99"

    new.d <- data.frame(new.d, tnmclinstagegroup)
  new.d <- apply_labels(new.d, tnmclinstagegroup = "tnm_clin_stage_group")

temp.d <- data.frame (new.d.1, tnmclinstagegroup)
summarytools::view(dfSummary(new.d$tnmclinstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tnmclinstagegroup [labelled, factor] tnm_clin_stage_group 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
784(40.5%)
26(1.3%)
421(21.7%)
233(12.0%)
52(2.7%)
107(5.5%)
313(16.2%)
 1621 (45.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
75(65.8%)
4(3.5%)
0(0.0%)
0(0.0%)
3(2.6%)
12(10.5%)
20(17.5%)
 207 (64.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
47(70.1%)
6(9.0%)
0(0.0%)
0(0.0%)
2(3.0%)
9(13.4%)
3(4.5%)
 143 (68.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
61(58.1%)
4(3.8%)
0(0.0%)
0(0.0%)
4(3.8%)
19(18.1%)
17(16.2%)
 210 (66.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
37(13.7%)
2(0.7%)
142(52.4%)
56(20.7%)
3(1.1%)
9(3.3%)
22(8.1%)
 85 (23.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
141(24.4%)
4(0.7%)
217(37.5%)
156(27.0%)
15(2.6%)
13(2.2%)
32(5.5%)
 7 (1.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
420(53.2%)
6(0.8%)
56(7.1%)
21(2.7%)
25(3.2%)
45(5.7%)
217(27.5%)
 964 (55.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 tnm_clin_stage_group [factor] 1. 1 2. 2 3. 2A 4. 2B 5. 3 6. 4 7. Unknown.99
3(27.3%)
0(0.0%)
6(54.5%)
0(0.0%)
0(0.0%)
0(0.0%)
2(18.2%)
 5 (31.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM CLIN T

  • Description: Detailed site-specific codes for the clinical tumor (T) as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • Blank Information not available to code this item.
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1001


All data

st_css() #IMPORTANT!
  ajcctnmclint <- as.factor(trimws(d[,"ajcctnmclint"]))
  levels(ajcctnmclint)[levels(ajcctnmclint)=="88"] <- "Not_applicable.88"

    new.d <- data.frame(new.d, ajcctnmclint)
  new.d <- apply_labels(new.d, ajcctnmclint = "ajcc_tnm_clin_t")

temp.d <- data.frame (new.d.1, ajcctnmclint)
summarytools::view(dfSummary(new.d$ajcctnmclint, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmclint [labelled, factor] ajcc_tnm_clin_t 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX
1(2.7%)
2(5.4%)
21(56.8%)
3(8.1%)
5(13.5%)
1(2.7%)
1(2.7%)
3(8.1%)
 3520 (99.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX
1(2.9%)
2(5.9%)
19(55.9%)
3(8.8%)
5(14.7%)
1(2.9%)
1(2.9%)
2(5.9%)
 322 (90.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX
0(0.0%)
0(0.0%)
1(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(50.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_t [factor] 1. Not_applicable.88 2. cT1a 3. cT1c 4. cT2 5. cT2a 6. cT3 7. cT3a 8. cTX All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM CLIN N

  • Description: Detailed site-specific codes for the clinical nodes (N) as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • Blank Information not available to code this item.
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1002


All data

st_css() #IMPORTANT!
  ajcctnmclinn <- as.factor(trimws(d[,"ajcctnmclinn"]))
  levels(ajcctnmclinn)[levels(ajcctnmclinn)=="88"] <- "Not_applicable.88"

    new.d <- data.frame(new.d, ajcctnmclinn)
  new.d <- apply_labels(new.d, ajcctnmclinn = "ajcc_tnm_clin_n")

temp.d <- data.frame (new.d.1, ajcctnmclinn)
summarytools::view(dfSummary(new.d$ajcctnmclinn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmclinn [labelled, factor] ajcc_tnm_clin_n 1. Not_applicable.88 2. cN0 3. cN1 4. cNX
1(2.5%)
36(90.0%)
2(5.0%)
1(2.5%)
 3517 (98.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX
1(2.8%)
33(91.7%)
1(2.8%)
1(2.8%)
 320 (89.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX
0(0.0%)
2(66.7%)
1(33.3%)
0(0.0%)
 1751 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_n [factor] 1. Not_applicable.88 2. cN0 3. cN1 4. cNX All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM CLIN M

  • Description: Detailed site-specific codes for the clinical metastases (M) as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • Blank Information not available to code this item.
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1003


All data

st_css() #IMPORTANT!
  ajcctnmclinm <- as.factor(trimws(d[,"ajcctnmclinm"]))
  levels(ajcctnmclinm)[levels(ajcctnmclinm)=="88"] <- "Not_applicable.88"

    new.d <- data.frame(new.d, ajcctnmclinm)
  new.d <- apply_labels(new.d, ajcctnmclinm = "ajcc_tnm_clin_m")

temp.d <- data.frame (new.d.1, ajcctnmclinm)
summarytools::view(dfSummary(new.d$ajcctnmclinm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmclinm [labelled, factor] ajcc_tnm_clin_m 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b
1(2.5%)
35(87.5%)
2(5.0%)
1(2.5%)
1(2.5%)
 3517 (98.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b
1(2.8%)
32(88.9%)
2(5.6%)
1(2.8%)
0(0.0%)
 320 (89.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b
0(0.0%)
2(66.7%)
0(0.0%)
0(0.0%)
1(33.3%)
 1751 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_m [factor] 1. Not_applicable.88 2. cM0 3. cM1b 4. cM1c 5. pM1b All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM CLIN STAGE GROUP

  • Description: Detailed site-specific codes for the clinical stage group as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • 99 Unknown, not staged
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1004


All data

st_css() #IMPORTANT!
  ajcctnmclinstagegroup <- as.factor(trimws(d[,"ajcctnmclinstagegroup"]))
  levels(ajcctnmclinstagegroup)[levels(ajcctnmclinstagegroup)=="88"] <- "Not_applicable.88"
  levels(ajcctnmclinstagegroup)[levels(ajcctnmclinstagegroup)=="99"] <- "Unknown.99"

    new.d <- data.frame(new.d, ajcctnmclinstagegroup)
  new.d <- apply_labels(new.d, ajcctnmclinstagegroup = "ajcc_tnm_clin_stage_group")

temp.d <- data.frame (new.d.1, ajcctnmclinstagegroup)
summarytools::view(dfSummary(new.d$ajcctnmclinstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmclinstagegroup [labelled, factor] ajcc_tnm_clin_stage_group 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99
9(18.0%)
2(4.0%)
10(20.0%)
6(12.0%)
1(2.0%)
1(2.0%)
1(2.0%)
4(8.0%)
1(2.0%)
15(30.0%)
 3507 (98.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99
8(17.4%)
2(4.3%)
9(19.6%)
6(13.0%)
1(2.2%)
1(2.2%)
1(2.2%)
3(6.5%)
1(2.2%)
14(30.4%)
 310 (87.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
1(33.3%)
 1751 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_clin_stage_group [factor] 1. 1 2. 2A 3. 2B 4. 2C 5. 3A 6. 3B 7. 3C 8. 4B 9. Not_applicable.88 10. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM PATH T

  • Description: Detailed site-specific codes for the pathologic tumor (T) as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • Blank Information not available to code this item.
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1011


All data

st_css() #IMPORTANT!
  ajcctnmpatht <- as.factor(trimws(d[,"ajcctnmpatht"]))
  levels(ajcctnmpatht)[levels(ajcctnmpatht)=="88"] <- "Not_applicable.88"

    new.d <- data.frame(new.d, ajcctnmpatht)
  new.d <- apply_labels(new.d, ajcctnmpatht = "ajcc_tnm_path_t")

temp.d <- data.frame (new.d.1, ajcctnmpatht)
summarytools::view(dfSummary(new.d$ajcctnmpatht, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmpatht [labelled, factor] ajcc_tnm_path_t 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b
1(7.1%)
1(7.1%)
8(57.1%)
3(21.4%)
1(7.1%)
 3543 (99.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b
1(7.7%)
0(0.0%)
8(61.5%)
3(23.1%)
1(7.7%)
 343 (96.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_t [factor] 1. Not_applicable.88 2. pT1b 3. pT2 4. pT3a 5. pT3b All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM PATH N

  • Description: Detailed site-specific codes for the pathologic nodes (N) as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • Blank Information not available to code this item.
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1012


All data

st_css() #IMPORTANT!
  ajcctnmpathn <- as.factor(trimws(d[,"ajcctnmpathn"]))
  levels(ajcctnmpathn)[levels(ajcctnmpathn)=="88"] <- "Not_applicable.88"

    new.d <- data.frame(new.d, ajcctnmpathn)
  new.d <- apply_labels(new.d, ajcctnmpathn = "ajcc_tnm_path_n")

temp.d <- data.frame (new.d.1, ajcctnmpathn)
summarytools::view(dfSummary(new.d$ajcctnmpathn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmpathn [labelled, factor] ajcc_tnm_path_n 1. Not_applicable.88 2. pN0 3. pNX
1(7.1%)
10(71.4%)
3(21.4%)
 3543 (99.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX
1(7.7%)
9(69.2%)
3(23.1%)
 343 (96.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX
0(0.0%)
1(100.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_n [factor] 1. Not_applicable.88 2. pN0 3. pNX All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM PATH M

  • Description: Detailed site-specific codes for the clinical path (M) as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan trajcctnmpathneatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • Blank Information not available to code this item.
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1013


All data

st_css() #IMPORTANT!
  ajcctnmpathm <- as.factor(trimws(d[,"ajcctnmpathm"]))
  levels(ajcctnmpathm)[levels(ajcctnmpathm)=="88"] <- "Not_applicable.88"

    new.d <- data.frame(new.d, ajcctnmpathm)
  new.d <- apply_labels(new.d, ajcctnmpathm = "ajcc_tnm_path_m")

temp.d <- data.frame (new.d.1, ajcctnmpathm)
summarytools::view(dfSummary(new.d$ajcctnmpathm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmpathm [labelled, factor] ajcc_tnm_path_m 1. Not_applicable.88 2. cM0
1(7.1%)
13(92.9%)
 3543 (99.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0
1(7.7%)
12(92.3%)
 343 (96.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0
0(0.0%)
1(100.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_m [factor] 1. Not_applicable.88 2. cM0 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


AJCC TNM PATH STAGE GROUP

  • Description: Detailed site-specific codes for the pathologic stage group as defined by the current AJCC edition.
  • Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
  • Codes (in addition to those published in the AJCC Cancer Staging Manual)
    • 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
    • 99 Unknown, not staged
  • Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1014


All data

st_css() #IMPORTANT!
  ajcctnmpathstagegroup <- as.factor(trimws(d[,"ajcctnmpathstagegroup"]))
  levels(ajcctnmpathstagegroup)[levels(ajcctnmpathstagegroup)=="88"] <- "Not_applicable.88"
  levels(ajcctnmpathstagegroup)[levels(ajcctnmpathstagegroup)=="99"] <- "Unknown.99"

    new.d <- data.frame(new.d, ajcctnmpathstagegroup)
  new.d <- apply_labels(new.d, ajcctnmpathstagegroup = "ajcc_tnm_path_stage_group")

temp.d <- data.frame (new.d.1, ajcctnmpathstagegroup)
summarytools::view(dfSummary(new.d$ajcctnmpathstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 ajcctnmpathstagegroup [labelled, factor] ajcc_tnm_path_stage_group 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99
1(2.0%)
3(6.0%)
1(2.0%)
2(4.0%)
2(4.0%)
1(2.0%)
40(80.0%)
 3507 (98.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99
0(0.0%)
3(6.5%)
1(2.2%)
2(4.3%)
2(4.3%)
1(2.2%)
37(80.4%)
 310 (87.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(66.7%)
 1751 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 ajcc_tnm_path_stage_group [factor] 1. 1B 2. 2B 3. 2C 4. 3B 5. 3C 6. Not_applicable.88 7. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


TUMOR MARKER 2

  • Description: Records prognostic indicators for specific sites or histologies. CoC offered these items for optional use for cases diagnosed 1996 and forward. See the CoC ROADS Manual, 1998 Supplement, for a list of specific sites and histologies.

  • For SEER requirements for the specific sites, histologies, and diagnosis years for which this item is coded, see the 1998 SEER Program Code Manual.

  • Codes

    • 0 None done (SX)
    • 1 Positive/elevated
    • 2 Negative/normal; within normal limits (S0)
    • 3 Borderline; undetermined whether positive/elevated or negative/normal

  • Three-tiered system:

    • 4 Range 1 (S1)
    • 5 Range 2 (S2)
    • 6 Range 3 (S3)
    • 8 Ordered, but results not in chart
    • 9 Not applicable

  • For sites for which Tumor Marker 2 is not collected:

    • 9 Not applicable

  • Note: As of January 1, 2003, this data item is no longer required or recommended by CoC. However, the item was collected in the past and it is recommended that historic data be retained.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1160


All data

st_css() #IMPORTANT!
  tumormarker2 <- as.factor(trimws(d[,"tumormarker2"]))

    new.d <- data.frame(new.d, tumormarker2)
  new.d <- apply_labels(new.d, tumormarker2 = "tumor_marker2")

temp.d <- data.frame (new.d.1, tumormarker2)
summarytools::view(dfSummary(new.d$tumormarker2, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 tumormarker2 [labelled, factor] tumor_marker2 1. 0 2. 9
8(24.2%)
25(75.8%)
 3524 (99.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE SURGERY

  • Description: Date the first surgery of the type described under Surgery of Primary Site, Scope of Regional Lymph Node Surgery, or Surgery of Other Regional Site(s), Distant Site(s) or Distant Lymph Nodes was performed. See also RX Summ–Surg Prim Site [1290], RX Summ–Scope Reg LN Sur [1292], and RX Summ–Surg Oth Reg/Dis [1294]. See Chapter X for date format. Formerly RX Date–Surgery.

  • Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1200


All data

  rxdatesurgery <- trimws(d[,"rxdatesurgery"])
  #new.d.n <- data.frame(new.d.n, rxdatesurgery) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatesurgery), F, substr(rxdatesurgery, start=7, stop=8)=="99")
  rxdatesurgery[select99] <- substr(rxdatesurgery[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatesurgery), F, nchar(trimws(rxdatesurgery))==6)
  rxdatesurgery[select6] <- paste(rxdatesurgery[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatesurgery), F, nchar(trimws(rxdatesurgery))==4)
  rxdatesurgery[select4] <- paste(rxdatesurgery[select4], "0615", sep="")
  
  rxdatesurgery <- as.Date(rxdatesurgery, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatesurgery)
  new.d <- apply_labels(new.d, rxdatesurgery = "rx_date_surgery")
  temp.d <- data.frame (new.d.1, rxdatesurgery)
summarytools::view(dfSummary(new.d$rxdatesurgery, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatesurgery [labelled, Date] rx_date_surgery min : 2012-05-15 med : 2016-06-15 max : 2019-08-15 range : 7y 3m 0d 553 distinct values 2135 (60.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] 1. 2015-02-15 2. 2015-03-15 3. 2015-04-15 4. 2015-05-15 5. 2015-06-15 6. 2015-07-15 7. 2015-08-15 8. 2015-09-15 9. 2015-10-15 10. 2015-11-15 11. 2015-12-15 12. 2016-01-15 13. 2016-02-15 14. 2016-03-15 15. 2016-04-15 16. 2016-05-15 17. 2016-06-15 18. 2016-07-15 19. 2016-08-15 20. 2016-09-15 21. 2016-10-15 22. 2016-11-15 23. 2016-12-15 24. 2017-01-15 25. 2017-02-15 26. 2017-03-15 27. 2017-04-15 28. 2017-05-15 29. 2017-06-15 30. 2017-08-15 31. 2017-09-15 32. 2017-10-15 33. 2017-11-15 34. 2017-12-15 35. 2018-01-15 36. 2018-03-15
1(0.6%)
4(2.6%)
2(1.3%)
2(1.3%)
5(3.2%)
4(2.6%)
5(3.2%)
6(3.9%)
9(5.8%)
6(3.9%)
6(3.9%)
3(1.9%)
6(3.9%)
6(3.9%)
4(2.6%)
6(3.9%)
4(2.6%)
6(3.9%)
5(3.2%)
8(5.2%)
4(2.6%)
9(5.8%)
5(3.2%)
5(3.2%)
2(1.3%)
4(2.6%)
5(3.2%)
3(1.9%)
1(0.6%)
2(1.3%)
2(1.3%)
8(5.2%)
1(0.6%)
1(0.6%)
3(1.9%)
2(1.3%)
 166 (51.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] 1. 2015-04-15 2. 2015-05-15 3. 2015-07-15 4. 2015-09-15 5. 2015-10-15 6. 2015-11-15 7. 2015-12-15 8. 2016-01-15 9. 2016-02-15 10. 2016-03-15 11. 2016-05-15 12. 2016-06-15 13. 2016-07-15 14. 2016-08-15 15. 2016-09-15 16. 2016-10-15 17. 2016-11-15 18. 2016-12-15 19. 2017-01-15 20. 2017-02-15 21. 2017-03-15 22. 2017-04-15 23. 2017-09-15 24. 2017-10-15 25. 2018-01-15 26. 2018-05-15
1(1.6%)
2(3.3%)
2(3.3%)
2(3.3%)
3(4.9%)
2(3.3%)
1(1.6%)
3(4.9%)
4(6.6%)
2(3.3%)
2(3.3%)
2(3.3%)
3(4.9%)
5(8.2%)
4(6.6%)
1(1.6%)
2(3.3%)
4(6.6%)
3(4.9%)
2(3.3%)
2(3.3%)
2(3.3%)
3(4.9%)
1(1.6%)
2(3.3%)
1(1.6%)
 149 (71.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] 1. 2015-01-15 2. 2015-02-15 3. 2015-03-15 4. 2015-04-15 5. 2015-05-15 6. 2015-06-15 7. 2015-07-15 8. 2015-08-15 9. 2015-09-15 10. 2015-10-15 11. 2015-11-15 12. 2015-12-15 13. 2016-01-15 14. 2016-02-15 15. 2016-03-15 16. 2016-04-15 17. 2016-05-15 18. 2016-06-15 19. 2016-07-15 20. 2016-08-15 21. 2016-09-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-01-15 26. 2017-02-15 27. 2017-03-15 28. 2017-04-15 29. 2017-05-15 30. 2017-08-15 31. 2017-09-15 32. 2017-10-15 33. 2017-11-15 34. 2018-01-15
2(1.2%)
1(0.6%)
1(0.6%)
5(3.0%)
2(1.2%)
6(3.6%)
2(1.2%)
6(3.6%)
13(7.7%)
3(1.8%)
7(4.2%)
8(4.8%)
9(5.4%)
9(5.4%)
7(4.2%)
4(2.4%)
11(6.5%)
3(1.8%)
6(3.6%)
6(3.6%)
12(7.1%)
9(5.4%)
4(2.4%)
2(1.2%)
13(7.7%)
3(1.8%)
4(2.4%)
2(1.2%)
3(1.8%)
1(0.6%)
1(0.6%)
1(0.6%)
1(0.6%)
1(0.6%)
 147 (46.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] min : 2012-05-15 med : 2016-06-15 max : 2019-08-15 range : 7y 3m 0d 63 distinct values 192 (53.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] min : 2015-02-19 med : 2016-08-17 max : 2018-07-23 range : 3y 5m 4d 224 distinct values 309 (52.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] min : 2015-01-05 med : 2016-05-19 max : 2018-06-06 range : 3y 5m 1d 389 distinct values 1163 (66.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_surgery [Date] 1. 2012-06-15 2. 2012-09-15 3. 2013-03-15 4. 2013-11-15 5. 2014-02-15 6. 2014-05-15 7. 2014-11-15
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
 9 (56.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE RADIATION

  • Description: Records the date on which radiation therapy began at any facility that is part of the first course of treatment. See Chapter X for date format. Use RX DATE RADIATION FLAG [1211] if there is no appropriate or known date for this item. Formerly RX Date–Radiation

  • Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1210


All data

  rxdateradiation <- trimws(d[,"rxdateradiation"])
  #new.d.n <- data.frame(new.d.n, rxdateradiation) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdateradiation), F, substr(rxdateradiation, start=7, stop=8)=="99")
  rxdateradiation[select99] <- substr(rxdateradiation[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdateradiation), F, nchar(trimws(rxdateradiation))==6)
  rxdateradiation[select6] <- paste(rxdateradiation[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdateradiation), F, nchar(trimws(rxdateradiation))==4)
  rxdateradiation[select4] <- paste(rxdateradiation[select4], "0615", sep="")
  
  rxdateradiation <- as.Date(rxdateradiation, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdateradiation)
  new.d <- apply_labels(new.d, rxdateradiation = "rx_date_radiation")
  temp.d <- data.frame (new.d.1, rxdateradiation)
summarytools::view(dfSummary(new.d$rxdateradiation, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdateradiation [labelled, Date] rx_date_radiation min : 2012-11-15 med : 2016-08-29 max : 2019-06-04 range : 6y 6m 20d 536 distinct values 2222 (62.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] 1. 2015-04-15 2. 2015-05-15 3. 2015-06-15 4. 2015-07-15 5. 2015-08-15 6. 2015-09-15 7. 2015-10-15 8. 2015-12-15 9. 2016-02-15 10. 2016-03-15 11. 2016-05-15 12. 2016-06-15 13. 2016-07-15 14. 2016-08-15 15. 2016-09-15 16. 2016-10-15 17. 2016-11-15 18. 2016-12-15 19. 2017-01-15 20. 2017-02-15 21. 2017-03-15 22. 2017-06-15 23. 2017-07-15 24. 2017-08-15 25. 2017-10-15 26. 2017-11-15 27. 2017-12-15 28. 2018-01-15 29. 2018-02-15 30. 2018-03-15 31. 2018-07-15
1(1.3%)
3(3.9%)
2(2.6%)
1(1.3%)
1(1.3%)
1(1.3%)
1(1.3%)
1(1.3%)
2(2.6%)
4(5.3%)
2(2.6%)
7(9.2%)
2(2.6%)
7(9.2%)
2(2.6%)
3(3.9%)
1(1.3%)
4(5.3%)
4(5.3%)
2(2.6%)
8(10.5%)
1(1.3%)
3(3.9%)
2(2.6%)
2(2.6%)
1(1.3%)
2(2.6%)
1(1.3%)
1(1.3%)
3(3.9%)
1(1.3%)
 245 (76.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] 1. 2015-03-15 2. 2015-04-15 3. 2015-05-15 4. 2015-06-15 5. 2015-07-15 6. 2015-08-15 7. 2015-09-15 8. 2015-10-15 9. 2015-11-15 10. 2015-12-15 11. 2016-01-15 12. 2016-02-15 13. 2016-03-15 14. 2016-04-15 15. 2016-05-15 16. 2016-06-15 17. 2016-07-15 18. 2016-08-15 19. 2016-09-15 20. 2016-10-15 21. 2016-11-15 22. 2016-12-15 23. 2017-01-15 24. 2017-02-15 25. 2017-03-15 26. 2017-05-15 27. 2017-06-15 28. 2017-07-15 29. 2017-08-15 30. 2017-10-15 31. 2017-11-15 32. 2018-03-15 33. 2018-04-15 34. 2018-05-15
1(1.1%)
2(2.2%)
1(1.1%)
2(2.2%)
1(1.1%)
3(3.3%)
4(4.4%)
2(2.2%)
2(2.2%)
3(3.3%)
3(3.3%)
2(2.2%)
2(2.2%)
2(2.2%)
4(4.4%)
4(4.4%)
3(3.3%)
2(2.2%)
8(8.9%)
3(3.3%)
4(4.4%)
3(3.3%)
4(4.4%)
5(5.6%)
6(6.7%)
2(2.2%)
4(4.4%)
1(1.1%)
1(1.1%)
2(2.2%)
1(1.1%)
1(1.1%)
1(1.1%)
1(1.1%)
 120 (57.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] 1. 2015-03-15 2. 2015-05-15 3. 2015-06-15 4. 2015-07-15 5. 2015-08-15 6. 2015-09-15 7. 2015-10-15 8. 2015-11-15 9. 2015-12-15 10. 2016-01-15 11. 2016-02-15 12. 2016-03-15 13. 2016-04-15 14. 2016-05-15 15. 2016-06-15 16. 2016-07-15 17. 2016-08-15 18. 2016-09-15 19. 2016-10-15 20. 2016-11-15 21. 2016-12-15 22. 2017-01-15 23. 2017-02-15 24. 2017-03-15 25. 2017-04-15 26. 2017-05-15 27. 2017-06-15 28. 2017-07-15 29. 2017-08-15 30. 2017-09-15 31. 2018-01-15 32. 2018-05-15
1(1.2%)
2(2.4%)
2(2.4%)
2(2.4%)
3(3.5%)
2(2.4%)
3(3.5%)
5(5.9%)
3(3.5%)
3(3.5%)
4(4.7%)
3(3.5%)
3(3.5%)
1(1.2%)
3(3.5%)
1(1.2%)
3(3.5%)
4(4.7%)
2(2.4%)
6(7.1%)
8(9.4%)
5(5.9%)
3(3.5%)
1(1.2%)
1(1.2%)
4(4.7%)
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
 230 (73.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] min : 2012-11-15 med : 2017-02-15 max : 2019-04-15 range : 6y 5m 0d 53 distinct values 226 (63.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] min : 2015-03-17 med : 2016-11-23 max : 2019-06-04 range : 4y 2m 18d 177 distinct values 376 (64.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] min : 2015-02-05 med : 2016-07-09 max : 2018-09-18 range : 3y 7m 13d 437 distinct values 1014 (57.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_radiation [Date] 1. 2013-05-15 2. 2013-06-15 3. 2014-07-15 4. 2015-01-15 5. 2016-06-15
1(20.0%)
1(20.0%)
1(20.0%)
1(20.0%)
1(20.0%)
 11 (68.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE CHEMO

  • Description: Date of initiation of chemotherapy that is part of the first course of treatment. See also RX Summ–Chemo [1390]. See Chapter X for date format. Formerly RX Date–Chemo.

  • Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes. Note: CoC discontinued support of this item from 2003 through 2009.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1220


All data

  rxdatechemo <- trimws(d[,"rxdatechemo"])
  #new.d.n <- data.frame(new.d.n, rxdatechemo) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatechemo), F, substr(rxdatechemo, start=7, stop=8)=="99")
  rxdatechemo[select99] <- substr(rxdatechemo[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatechemo), F, nchar(trimws(rxdatechemo))==6)
  rxdatechemo[select6] <- paste(rxdatechemo[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatechemo), F, nchar(trimws(rxdatechemo))==4)
  rxdatechemo[select4] <- paste(rxdatechemo[select4], "0615", sep="")
  
  rxdatechemo <- as.Date(rxdatechemo, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatechemo)
  new.d <- apply_labels(new.d, rxdatechemo = "rx_date_chemo")
  temp.d <- data.frame (new.d.1, rxdatechemo)
summarytools::view(dfSummary(new.d$rxdatechemo, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatechemo [labelled, Date] rx_date_chemo min : 2015-03-15 med : 2016-10-18 max : 2018-05-15 range : 3y 2m 0d 31 distinct values 3521 (99.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] 1. 2016-08-15 2. 2016-12-15 3. 2017-04-15 4. 2017-12-15
1(25.0%)
1(25.0%)
1(25.0%)
1(25.0%)
 317 (98.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] 1. 2016-06-15 2. 2016-12-15
3(75.0%)
1(25.0%)
 206 (98.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] 1. 2015-03-15 2. 2015-09-15 3. 2016-04-15 4. 2016-06-15 5. 2016-10-15 6. 2017-01-15 7. 2017-02-15
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
 308 (97.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] 1. 2015-12-15 2. 2018-05-15
1(50.0%)
1(50.0%)
 354 (99.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] 1. 2015-04-23 2. 2017-02-15
1(50.0%)
1(50.0%)
 583 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] 1. 2015-05-15 2. 2015-10-22 3. 2015-12-02 4. 2016-01-20 5. 2016-02-23 6. 2016-06-08 7. 2016-07-20 8. 2016-10-21 9. 2016-11-11 10. 2016-11-22 11. 2016-11-30 12. 2016-12-06 13. 2017-01-19 14. 2017-02-02 15. 2017-05-16 16. 2017-06-01 17. 2018-04-03
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
1(5.9%)
 1737 (99.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_chemo [Date] All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE HORMONE

  • Description: Date of initiation for hormone therapy that is part of the first course of treatment. See also RX Summ–Hormone [1400]. See Chapter X for date format. Formerly RX Date–Hormone.

  • Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes. Note: CoC discontinued support of this item from 2003 through 2009.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1230


All data

  rxdatehormone <- trimws(d[,"rxdatehormone"])
  #new.d.n <- data.frame(new.d.n, rxdatehormone) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatehormone), F, substr(rxdatehormone, start=7, stop=8)=="99")
  rxdatehormone[select99] <- substr(rxdatehormone[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatehormone), F, nchar(trimws(rxdatehormone))==6)
  rxdatehormone[select6] <- paste(rxdatehormone[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatehormone), F, nchar(trimws(rxdatehormone))==4)
  rxdatehormone[select4] <- paste(rxdatehormone[select4], "0615", sep="")
  
  rxdatehormone <- as.Date(rxdatehormone, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatehormone)
  new.d <- apply_labels(new.d, rxdatehormone = "rx_date_hormone")
  temp.d <- data.frame (new.d.1, rxdatehormone)
summarytools::view(dfSummary(new.d$rxdatehormone, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatehormone [labelled, Date] rx_date_hormone min : 2013-03-15 med : 2016-07-25 max : 2019-04-15 range : 6y 1m 0d 353 distinct values 2855 (80.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] 1. 2015-01-15 2. 2015-04-15 3. 2015-05-15 4. 2015-09-15 5. 2015-11-15 6. 2016-01-15 7. 2016-02-15 8. 2016-03-15 9. 2016-04-15 10. 2016-05-15 11. 2016-06-15 12. 2016-07-15 13. 2016-08-15 14. 2016-09-15 15. 2016-10-15 16. 2016-12-15 17. 2017-01-15 18. 2017-02-15 19. 2017-03-15 20. 2017-04-15 21. 2017-05-15 22. 2017-06-15 23. 2017-07-15 24. 2017-09-15 25. 2017-10-15 26. 2017-11-15 27. 2017-12-15 28. 2018-01-15
1(1.4%)
2(2.8%)
2(2.8%)
1(1.4%)
2(2.8%)
2(2.8%)
1(1.4%)
1(1.4%)
4(5.6%)
1(1.4%)
7(9.7%)
3(4.2%)
3(4.2%)
5(6.9%)
4(5.6%)
6(8.3%)
2(2.8%)
2(2.8%)
2(2.8%)
1(1.4%)
1(1.4%)
4(5.6%)
1(1.4%)
3(4.2%)
4(5.6%)
2(2.8%)
3(4.2%)
2(2.8%)
 249 (77.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] 1. 2015-02-15 2. 2015-04-15 3. 2015-05-15 4. 2015-06-15 5. 2015-08-15 6. 2015-09-15 7. 2015-10-15 8. 2015-11-15 9. 2015-12-15 10. 2016-01-15 11. 2016-02-15 12. 2016-03-15 13. 2016-04-15 14. 2016-05-15 15. 2016-06-15 16. 2016-08-15 17. 2016-09-15 18. 2016-10-15 19. 2016-11-15 20. 2016-12-15 21. 2017-02-15 22. 2017-03-15 23. 2017-04-15 24. 2017-05-15 25. 2017-06-15 26. 2017-07-15 27. 2017-12-15 28. 2018-01-15
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
3(5.9%)
1(2.0%)
1(2.0%)
1(2.0%)
2(3.9%)
2(3.9%)
4(7.8%)
3(5.9%)
3(5.9%)
1(2.0%)
2(3.9%)
1(2.0%)
3(5.9%)
3(5.9%)
4(7.8%)
1(2.0%)
2(3.9%)
1(2.0%)
2(3.9%)
1(2.0%)
3(5.9%)
1(2.0%)
 159 (75.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] 1. 2015-02-15 2. 2015-03-15 3. 2015-04-15 4. 2015-05-15 5. 2015-08-15 6. 2015-09-15 7. 2015-10-15 8. 2015-11-15 9. 2015-12-15 10. 2016-01-15 11. 2016-02-15 12. 2016-03-15 13. 2016-04-15 14. 2016-05-15 15. 2016-06-15 16. 2016-07-15 17. 2016-08-15 18. 2016-09-15 19. 2016-10-15 20. 2016-11-15 21. 2017-01-15 22. 2017-02-15 23. 2017-03-15 24. 2017-04-15 25. 2017-05-15
1(1.7%)
1(1.7%)
2(3.4%)
3(5.1%)
3(5.1%)
5(8.5%)
3(5.1%)
2(3.4%)
1(1.7%)
3(5.1%)
1(1.7%)
3(5.1%)
2(3.4%)
2(3.4%)
4(6.8%)
4(6.8%)
2(3.4%)
3(5.1%)
5(8.5%)
3(5.1%)
1(1.7%)
1(1.7%)
1(1.7%)
1(1.7%)
2(3.4%)
 256 (81.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] 1. 2013-03-15 2. 2013-05-15 3. 2013-06-15 4. 2013-08-15 5. 2014-08-15 6. 2014-09-15 7. 2015-01-15 8. 2015-02-15 9. 2015-03-15 10. 2015-04-15 11. 2015-06-15 12. 2015-07-15 13. 2015-08-15 14. 2015-09-15 15. 2015-10-15 16. 2015-11-15 17. 2015-12-15 18. 2016-02-15 19. 2016-03-15 20. 2016-07-15 21. 2016-08-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-04-15 26. 2017-05-15 27. 2017-06-15 28. 2017-07-15 29. 2017-08-15 30. 2017-09-15 31. 2017-11-15 32. 2017-12-15 33. 2018-01-15 34. 2018-03-15 35. 2018-04-15 36. 2018-05-15 37. 2018-06-15 38. 2018-07-15 39. 2018-08-15 40. 2018-09-15 41. 2018-10-15 42. 2018-11-15 43. 2018-12-15 44. 2019-02-15 45. 2019-04-15
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
4(4.9%)
3(3.7%)
2(2.4%)
2(2.4%)
1(1.2%)
3(3.7%)
2(2.4%)
2(2.4%)
2(2.4%)
2(2.4%)
5(6.1%)
1(1.2%)
2(2.4%)
2(2.4%)
1(1.2%)
2(2.4%)
5(6.1%)
1(1.2%)
2(2.4%)
2(2.4%)
1(1.2%)
3(3.7%)
4(4.9%)
3(3.7%)
1(1.2%)
1(1.2%)
3(3.7%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
 274 (77.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] min : 2015-02-10 med : 2016-09-20 max : 2019-02-28 range : 4y 0m 18d 129 distinct values 440 (75.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] min : 2015-01-16 med : 2016-06-14 max : 2018-05-29 range : 3y 4m 13d 229 distinct values 1462 (83.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_hormone [Date] 1. 2013-04-15
1(100.0%)
 15 (93.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE BRM

  • Description: Date of initiation for immunotherapy (a.k.a. biological response modifier) that is part of the first course of treatment. See also RX Summ–BRM [1410]. See Chapter X for date format. Formerly RX Date–BRM.

  • Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first course of therapy and to reconstruct the sequence of first-course treatment modes. Note: CoC discontinued support of this item from 2003 through 2009.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1240


All data

  rxdatebrm <- trimws(d[,"rxdatebrm"])
  #new.d.n <- data.frame(new.d.n, rxdatebrm) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatebrm), F, substr(rxdatebrm, start=7, stop=8)=="99")
  rxdatebrm[select99] <- substr(rxdatebrm[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatebrm), F, nchar(trimws(rxdatebrm))==6)
  rxdatebrm[select6] <- paste(rxdatebrm[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatebrm), F, nchar(trimws(rxdatebrm))==4)
  rxdatebrm[select4] <- paste(rxdatebrm[select4], "0615", sep="")
  
  rxdatebrm <- as.Date(rxdatebrm, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatebrm)
  new.d <- apply_labels(new.d, rxdatebrm = "rx_date_brm")
  temp.d <- data.frame (new.d.1, rxdatebrm)
summarytools::view(dfSummary(new.d$rxdatebrm, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatebrm [labelled, Date] rx_date_brm min : 2015-12-30 med : 2016-11-25 max : 2017-05-15 range : 1y 4m 15d 6 distinct values 3551 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] 1. 2017-05-15
1(100.0%)
 314 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] 1. 2015-12-30 2. 2016-03-08 3. 2016-09-21 4. 2017-01-30 5. 2017-02-15
1(20.0%)
1(20.0%)
1(20.0%)
1(20.0%)
1(20.0%)
 1749 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_brm [Date] All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE OTHER

  • Description: Date of initiation for other treatment that is part of the first course of treatment at any facility. See RX Summ–Other [1420]. See Chapter X for date format. Formerly RX Date–Other.

  • Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1250


All data

  rxdateother <- trimws(d[,"rxdateother"])
  #new.d.n <- data.frame(new.d.n, rxdateother) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdateother), F, substr(rxdateother, start=7, stop=8)=="99")
  rxdateother[select99] <- substr(rxdateother[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdateother), F, nchar(trimws(rxdateother))==6)
  rxdateother[select6] <- paste(rxdateother[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdateother), F, nchar(trimws(rxdateother))==4)
  rxdateother[select4] <- paste(rxdateother[select4], "0615", sep="")
  
  rxdateother <- as.Date(rxdateother, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdateother)
  new.d <- apply_labels(new.d, rxdateother = "rx_date_other")
  temp.d <- data.frame (new.d.1, rxdateother)
summarytools::view(dfSummary(new.d$rxdateother, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdateother [labelled, Date] rx_date_other 1. 2015-09-11 2. 2016-05-15 3. 2016-07-11 4. 2017-12-20
1(25.0%)
1(25.0%)
1(25.0%)
1(25.0%)
 3553 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] 1. 2016-05-15
1(100.0%)
 314 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] 1. 2015-09-11 2. 2016-07-11 3. 2017-12-20
1(33.3%)
1(33.3%)
1(33.3%)
 1751 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_other [Date] All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DATE INITIAL RX SEER

  • Description: Date of initiation of the first course therapy for the tumor being reported, using the SEER definition of first course. See also Date 1st Crs RX CoC [1270]. See Chapter V, Unresolved Issues, for further discussion of the difference between SEER and CoC items. See Chapter X for date format. Use Date Initial RX SEER Flag [1261] if there is no appropriate or known date for this item. Formerly Date of Initial RX–SEER.

  • Clarification of NPCR Required Status: Central registries funded by NPCR are required to collect either Date Initial RX SEER [1260] or Date 1st Crs RX CoC [1270].

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1260


All data

  dateinitialrxseer <- trimws(d[,"dateinitialrxseer"])
  #new.d.n <- data.frame(new.d.n, dateinitialrxseer) # keep NAACCR coding
  
  select99 <- ifelse(is.na(dateinitialrxseer), F, substr(dateinitialrxseer, start=7, stop=8)=="99")
  dateinitialrxseer[select99] <- substr(dateinitialrxseer[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(dateinitialrxseer), F, nchar(trimws(dateinitialrxseer))==6)
  dateinitialrxseer[select6] <- paste(dateinitialrxseer[select6], "15", sep="")
  
  select4 <- ifelse(is.na(dateinitialrxseer), F, nchar(trimws(dateinitialrxseer))==4)
  dateinitialrxseer[select4] <- paste(dateinitialrxseer[select4], "0615", sep="")
  
  dateinitialrxseer <- as.Date(dateinitialrxseer, c("%Y%m%d"))
  new.d <- data.frame(new.d, dateinitialrxseer)
  new.d <- apply_labels(new.d, dateinitialrxseer = "date_initial_rx_seer")
  temp.d <- data.frame (new.d.1, dateinitialrxseer)
summarytools::view(dfSummary(new.d$dateinitialrxseer, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 dateinitialrxseer [labelled, Date] date_initial_rx_seer min : 2012-05-15 med : 2016-06-29 max : 2019-08-15 range : 7y 3m 0d 727 distinct values 825 (23.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] 1. 2015-01-15 2. 2015-02-15 3. 2015-03-15 4. 2015-04-15 5. 2015-05-15 6. 2015-06-15 7. 2015-07-15 8. 2015-08-15 9. 2015-09-15 10. 2015-10-15 11. 2015-11-15 12. 2015-12-15 13. 2016-01-15 14. 2016-02-15 15. 2016-03-15 16. 2016-04-15 17. 2016-05-15 18. 2016-06-15 19. 2016-07-15 20. 2016-08-15 21. 2016-09-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-01-15 26. 2017-02-15 27. 2017-03-15 28. 2017-04-15 29. 2017-05-15 30. 2017-06-15 31. 2017-07-15 32. 2017-08-15 33. 2017-09-15 34. 2017-10-15 35. 2017-11-15 36. 2017-12-15 37. 2018-01-15 38. 2018-02-15 39. 2018-03-15 40. 2018-07-15
1(0.4%)
1(0.4%)
3(1.2%)
5(2.1%)
5(2.1%)
6(2.5%)
5(2.1%)
6(2.5%)
8(3.3%)
9(3.7%)
7(2.9%)
6(2.5%)
5(2.1%)
7(2.9%)
9(3.7%)
7(2.9%)
9(3.7%)
11(4.5%)
10(4.1%)
11(4.5%)
13(5.4%)
11(4.5%)
9(3.7%)
9(3.7%)
9(3.7%)
4(1.7%)
10(4.1%)
5(2.1%)
4(1.7%)
3(1.2%)
1(0.4%)
2(0.8%)
4(1.7%)
12(5.0%)
2(0.8%)
4(1.7%)
4(1.7%)
1(0.4%)
3(1.2%)
1(0.4%)
 79 (24.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] 1. 2015-02-15 2. 2015-03-15 3. 2015-04-15 4. 2015-05-15 5. 2015-06-15 6. 2015-07-15 7. 2015-08-15 8. 2015-09-15 9. 2015-10-15 10. 2015-11-15 11. 2015-12-15 12. 2016-01-15 13. 2016-02-15 14. 2016-03-15 15. 2016-04-15 16. 2016-05-15 17. 2016-06-15 18. 2016-07-15 19. 2016-08-15 20. 2016-09-15 21. 2016-10-15 22. 2016-11-15 23. 2016-12-15 24. 2017-01-15 25. 2017-02-15 26. 2017-03-15 27. 2017-04-15 28. 2017-05-15 29. 2017-06-15 30. 2017-08-15 31. 2017-09-15 32. 2017-10-15 33. 2017-11-15 34. 2017-12-15 35. 2018-01-15 36. 2018-04-15 37. 2018-05-15
1(0.6%)
1(0.6%)
4(2.6%)
4(2.6%)
3(1.9%)
2(1.3%)
2(1.3%)
7(4.5%)
7(4.5%)
5(3.2%)
3(1.9%)
5(3.2%)
7(4.5%)
5(3.2%)
3(1.9%)
6(3.9%)
7(4.5%)
5(3.2%)
7(4.5%)
12(7.7%)
3(1.9%)
8(5.2%)
8(5.2%)
6(3.9%)
8(5.2%)
6(3.9%)
4(2.6%)
1(0.6%)
1(0.6%)
1(0.6%)
3(1.9%)
1(0.6%)
1(0.6%)
3(1.9%)
3(1.9%)
1(0.6%)
1(0.6%)
 55 (26.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] 1. 2015-01-15 2. 2015-02-15 3. 2015-03-15 4. 2015-04-15 5. 2015-05-15 6. 2015-06-15 7. 2015-07-15 8. 2015-08-15 9. 2015-09-15 10. 2015-10-15 11. 2015-11-15 12. 2015-12-15 13. 2016-01-15 14. 2016-02-15 15. 2016-03-15 16. 2016-04-15 17. 2016-05-15 18. 2016-06-15 19. 2016-07-15 20. 2016-08-15 21. 2016-09-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-01-15 26. 2017-02-15 27. 2017-03-15 28. 2017-04-15 29. 2017-05-15 30. 2017-06-15 31. 2017-08-15 32. 2017-09-15 33. 2017-10-15 34. 2017-11-15 35. 2018-01-15
2(0.8%)
2(0.8%)
3(1.2%)
7(2.7%)
5(1.9%)
7(2.7%)
3(1.2%)
12(4.7%)
19(7.4%)
7(2.7%)
11(4.3%)
10(3.9%)
12(4.7%)
13(5.1%)
9(3.5%)
6(2.3%)
14(5.4%)
7(2.7%)
10(3.9%)
11(4.3%)
17(6.6%)
11(4.3%)
12(4.7%)
5(1.9%)
17(6.6%)
3(1.2%)
6(2.3%)
4(1.6%)
5(1.9%)
1(0.4%)
1(0.4%)
1(0.4%)
1(0.4%)
1(0.4%)
2(0.8%)
 58 (18.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] min : 2012-05-15 med : 2016-10-15 max : 2019-08-15 range : 7y 3m 0d 75 distinct values 69 (19.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] min : 2015-02-10 med : 2016-09-19 max : 2019-02-28 range : 4y 0m 18d 342 distinct values 112 (19.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] min : 2015-01-05 med : 2016-05-26 max : 2018-07-09 range : 3y 6m 4d 582 distinct values 447 (25.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_initial_rx_seer [Date] 1. 2012-06-15 2. 2012-09-15 3. 2013-03-15 4. 2013-04-15 5. 2013-06-15 6. 2013-11-15 7. 2014-02-15 8. 2014-05-15 9. 2014-11-15 10. 2015-01-15 11. 2016-06-15
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
 5 (31.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE DX/STG PROC

  • Description: Records the date on which the surgical diagnostic and/or staging procedure was performed. See Surgical and Diagnostic Staging Procedure [1350]. See Chapter X for date format. Formerly RX Date–DX/Stg Proc.

  • Note: This is a CoC item and for tumors diagnosed from January 1, 1996, through December 31, 2002, this may have been the date on which diagnostic, staging, and palliative procedures were performed. Beginning with tumors diagnosed on or after January 1, 2003, palliative procedures are collected in RX Summ–Palliative Proc [3270] and RX Hosp–Palliative Proc [3280].

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1280


All data

  rxdatedxstgproc <- trimws(d[,"rxdatedxstgproc"])
  #new.d.n <- data.frame(new.d.n, rxdatedxstgproc) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatedxstgproc), F, substr(rxdatedxstgproc, start=7, stop=8)=="99")
  rxdatedxstgproc[select99] <- substr(rxdatedxstgproc[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatedxstgproc), F, nchar(trimws(rxdatedxstgproc))==6)
  rxdatedxstgproc[select6] <- paste(rxdatedxstgproc[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatedxstgproc), F, nchar(trimws(rxdatedxstgproc))==4)
  rxdatedxstgproc[select4] <- paste(rxdatedxstgproc[select4], "0615", sep="")
  
  rxdatedxstgproc <- as.Date(rxdatedxstgproc, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatedxstgproc)
  new.d <- apply_labels(new.d, rxdatedxstgproc = "rx_date_dxstg_proc")
  temp.d <- data.frame (new.d.1, rxdatedxstgproc)
summarytools::view(dfSummary(new.d$rxdatedxstgproc, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatedxstgproc [labelled, Date] rx_date_dxstg_proc min : 2015-01-01 med : 2016-07-15 max : 2018-12-18 range : 3y 11m 17d 360 distinct values 2994 (84.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] min : 2015-01-01 med : 2016-07-15 max : 2018-12-18 range : 3y 11m 17d 360 distinct values 22 (3.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_dxstg_proc [Date] All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


# RX SUMM–TREATMENT STATUS {.tabset} - Description: This data item is a summary of the status for all treatment modalities. It is used in conjunction with Date Initial RX SEER [1260] and/or Date 1st Crs RX CoC [1270] and each modality of treatment with their respective date field to document whether treatment was given or not given, whether it is unknown if treatment was given, or whether treatment was given on an unknown date. Also indicates active surveillance (watchful waiting). This data item is effective for January 2010+ diagnoses.

  • Rationale: This field will document active surveillance (watchful waiting) and eliminate searching each treatment modality to determine whether treatment was given.

  • Codes

    • 0 No treatment given
    • 1 Treatment given
    • 2 Active surveillance (watchful waiting)
    • 9 Unknown if treatment was given

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1285


All data

st_css() #IMPORTANT!
  rxsummtreatmentstatus <- as.factor(trimws(d[,"rxsummtreatmentstatus"]))
  levels(rxsummtreatmentstatus) <- list(    No_treatment_given.0="0",
                                          Treatment_given.1="1",
                                          Active.2="2",
                                          Unknown.9="9"
                                          )
    rxsummtreatmentstatus <- relevel(rxsummtreatmentstatus, ref="Treatment_given.1")
    new.d <- data.frame(new.d, rxsummtreatmentstatus)
    new.d <- apply_labels(new.d, rxsummtreatmentstatus = "Status for all treatment modalities")
    #summary(new.d$rxsummtreatmentstatus)
temp.d <- data.frame (new.d.1, rxsummtreatmentstatus)
summarytools::view(dfSummary(new.d$rxsummtreatmentstatus, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummtreatmentstatus [labelled, factor] Status for all treatment modalities 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
2727(76.7%)
482(13.6%)
312(8.8%)
36(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
241(75.1%)
31(9.7%)
39(12.1%)
10(3.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
150(71.4%)
15(7.1%)
41(19.5%)
4(1.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
254(80.6%)
17(5.4%)
40(12.7%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
287(80.6%)
39(11.0%)
30(8.4%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
473(80.9%)
52(8.9%)
43(7.4%)
17(2.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
1311(74.7%)
325(18.5%)
117(6.7%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_treatment_status [factor] 1. Treatment_given.1 2. No_treatment_given.0 3. Active.2 4. Unknown.9
11(68.8%)
3(18.8%)
2(12.5%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SURG PRIM SITE

  • Description: Site-specific codes for the type of surgery to the primary site performed as part of the first course of treatment. This includes treatment given at all facilities as part of the first course of treatment.

  • Codes: (in addition to the site-specific codes; Refer to the most recent version of STORE and SEER Program Code manual for additional instructions.)

    • 00 None
    • 10-19 Site-specific code; tumor destruction
    • 20-80 Site-specific codes; resection
    • 90 Surgery, NOS
    • 98 Site specific codes; special
    • 99 Unknown

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1290


All data

st_css() #IMPORTANT!
  # Put 10-19 into one catecory and 20-80 into one category
  a<-as.numeric(d$rxsummsurgprimsite)
  a[which(a>=10&a<=19)]<-10
  a[which(a>=20&a<=80)]<-20
  rxsummsurgprimsite <- as.factor(a)
  levels(rxsummsurgprimsite) <- list(None.0="0",
                                  Tumor_destruction.10="10",
                                  Resection.20="20",
                                  Surgery_NOS.90="90",
                                  Unknown.99="99"
                                  )
    new.d <- data.frame(new.d, rxsummsurgprimsite)
    new.d <- apply_labels(new.d, rxsummsurgprimsite = "Type of radiation therapy")
    #summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, rxsummsurgprimsite)
summarytools::view(dfSummary(new.d$rxsummsurgprimsite, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummsurgprimsite [labelled, factor] Type of radiation therapy 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
2139(60.1%)
19(0.5%)
1383(38.9%)
0(0.0%)
16(0.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
165(51.4%)
1(0.3%)
154(48.0%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
149(71.0%)
2(1.0%)
59(28.1%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
146(46.3%)
1(0.3%)
167(53.0%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
196(55.1%)
3(0.8%)
157(44.1%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
295(50.4%)
1(0.2%)
275(47.0%)
0(0.0%)
14(2.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
1179(67.2%)
10(0.6%)
565(32.2%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_prim_site [factor] 1. None.0 2. Tumor_destruction.10 3. Resection.20 4. Surgery_NOS.90 5. Unknown.99
9(56.2%)
1(6.2%)
6(37.5%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SCOPE REG LN SUR

  • Description: Describes the removal, biopsy or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event at all facilities.

  • Rationale: In evaluating quality-of-care and treatment practices it is important to identify the removal, biopsy, or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event.

  • Codes (Refer to the most recent versions of STORE and the SEER Program Code Manual for instructions that should be applied to all surgically treated cases for all types of cancers.) The treatment of breast and skin cancers are where the distinction between sentinel lymph node biopsies (SLNBx) and more extensive dissection of regional lymph nodes is most frequently encountered. For all other sites, non-sentinel regional node dissections are typical, and codes 2, 6 and 7 are infrequently used.

    • 0 None
    • 1 Biopsy or aspiration of regional lymph node, NOS
    • 2 Sentinel lymph node biopsy
    • 3 Number of regional lymph nodes removed unknown, not stated; regional lymph nodes removed, NOS
    • 4 1 to 3 regional lymph nodes removed
    • 5 4 or more regional lymph nodes removed
    • 6 Sentinel node biopsy and code 3, 4, or 5 at same time or timing not noted
    • 7 Sentinel node biopsy and code 3, 4, or 5 at different times
    • 9 Unknown or not applicable

  • Note: One important use of registry data is the tracking of treatment patterns over time. To compare contemporary treatment to previously published treatment based on former codes, or to data unmodified from pre-1998 definitions, the ability to differentiate surgeries in which four or more regional lymph nodes are removed is desirable. However, it is very important to note that the distinction between codes 4 and 5 is made to permit comparison of current surgical procedures with procedures coded in the past when the removal of fewer than 4 nodes was not reflected in surgery codes. It is not intended to reflect clinical significance when applied to a particular surgical procedure. It is important to avoid inferring, by data presentation or other methods, that one category is preferable to another within the intent of these items.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1292


All data

st_css() #IMPORTANT!
  # Put 10-19 into one catecory and 20-80 into one category
  rxsummscopereglnsur <- as.factor(trimws(d[,"rxsummscopereglnsur"]))
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="0"] <- "None.0"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="1"] <- "Biopsy_or_aspiration_nodes.1"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="2"] <- "Sentinel_biopsy.2"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="3"] <- "Number_of_removed_unknown.3"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="4"] <- "1_3_removed.4"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="5"] <- "4_more_removed.5"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="6"] <- "code_345_same_time.6"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="7"] <- "code_345_diff_time.7"
  levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="9"] <- "Unknown.9"
  
  
    new.d <- data.frame(new.d, rxsummscopereglnsur)
    new.d <- apply_labels(new.d, rxsummscopereglnsur = "rx_summ_scope_reg_ln_sur")
    #summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, rxsummscopereglnsur)
summarytools::view(dfSummary(new.d$rxsummscopereglnsur, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummscopereglnsur [labelled, factor] rx_summ_scope_reg_ln_sur 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
2614(73.5%)
5(0.1%)
1(0.0%)
9(0.3%)
227(6.4%)
683(19.2%)
2(0.1%)
16(0.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
227(70.7%)
0(0.0%)
0(0.0%)
0(0.0%)
12(3.7%)
82(25.5%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
164(78.1%)
0(0.0%)
0(0.0%)
0(0.0%)
9(4.3%)
37(17.6%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
194(61.6%)
0(0.0%)
0(0.0%)
1(0.3%)
29(9.2%)
90(28.6%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
238(66.9%)
1(0.3%)
0(0.0%)
4(1.1%)
20(5.6%)
93(26.1%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
338(57.8%)
1(0.2%)
0(0.0%)
2(0.3%)
75(12.8%)
155(26.5%)
0(0.0%)
14(2.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
1442(82.2%)
3(0.2%)
1(0.1%)
2(0.1%)
80(4.6%)
223(12.7%)
2(0.1%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_scope_reg_ln_sur [factor] 1. None.0 2. Biopsy_or_aspiration_node 3. Sentinel_biopsy.2 4. Number_of_removed_unknown 5. 1_3_removed.4 6. 4_more_removed.5 7. code_345_same_time.6 8. Unknown.9
11(68.8%)
0(0.0%)
0(0.0%)
0(0.0%)
2(12.5%)
3(18.8%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SURG OTH REG/DIS

  • Description: Records the surgical removal of distant lymph nodes or other tissue(s)/organ(s) beyond the primary site.
  • Rationale: The removal of non-primary tissue documents the extent of surgical treatment and is useful in evaluating the extent of metastatic involvement.
  • Codes (Refer to the most recent version of STORE and SEER Program Code Manual for additional instructions.)
    • 0 None; diagnosed at autopsy
    • 1 Non-primary surgical procedure performed
    • 2 Non-primary surgical procedure to other regional sites
    • 3 Non-primary surgical procedure to distant lymph node(s)
    • 4 Non-primary surgical procedure to distant site
    • 5 Any combination of codes 2, 3, or 4
    • 9 Unknown; death certificate only
  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1294


All data

st_css() #IMPORTANT!
  # Put 10-19 into one catecory and 20-80 into one category
  rxsummsurgothregdis <- as.factor(trimws(d[,"rxsummsurgothregdis"]))
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="0"] <- "None.0"
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="1"] <- "Procedure_performed.1"
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="2"] <- "Other_regional_sites.2"
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="3"] <- "Distant_lymph_node.3"
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="4"] <- "Distant_site.4"
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="5"] <- "Combination_of_234.5"
  levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="9"] <- "Unknown.9"
  
  
    new.d <- data.frame(new.d, rxsummsurgothregdis)
    new.d <- apply_labels(new.d, rxsummsurgothregdis = "rx_summ_surg_oth_reg_dis")
    #summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, rxsummsurgothregdis)
summarytools::view(dfSummary(new.d$rxsummsurgothregdis, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummsurgothregdis [labelled, factor] rx_summ_surg_oth_reg_dis 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
3518(98.9%)
11(0.3%)
5(0.1%)
4(0.1%)
1(0.0%)
2(0.1%)
16(0.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
320(99.7%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
210(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
312(99.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
355(99.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
566(96.8%)
3(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
15(2.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
1740(99.2%)
8(0.5%)
4(0.2%)
2(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_oth_reg_dis [factor] 1. None.0 2. Procedure_performed.1 3. Other_regional_sites.2 4. Distant_lymph_node.3 5. Distant_site.4 6. Combination_of_234.5 7. Unknown.9
15(93.8%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


REASON FOR NO SURGERY

  • Description: Records the reason that no surgery was performed on the primary site.
  • Rationale: This data item provides information related to the quality of care and describes why primary site surgery was not performed.
  • Codes
    • 0 Surgery of the primary site was performed.
    • 1 Surgery of the primary site was not performed because it was not part of the planned first-course treatment.
    • 2 Surgery of the primary site was not recommended/performed because it was contraindicated due to patient risk factors (comorbid conditions, advanced age, etc.).
    • 5 Surgery of the primary site was not performed because the patient died prior to planned or recommended surgery.
    • 6 Surgery of the primary site was not performed; it was recommended by the patient’s physician, but was not performed as part of the first-course therapy. No reason was noted in the patient’s record.
    • 7 Surgery of the primary site was not performed; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
    • 8 Surgery of the primary site was recommended, but it is unknown if it was performed. Further follow-up is recommended.
    • 9 It is unknown if surgery of the primary site was recommended or performed. Death certificate-only cases and autopsy-only cases.
  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1340


All data

st_css() #IMPORTANT!
  # Put 10-19 into one catecory and 20-80 into one category
  reasonfornosurgery <- as.factor(trimws(d[,"reasonfornosurgery"]))
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="0"] <- "Site_performed.0"
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="1"] <- "not_part_of_planned.1"
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="2"] <- "Other_regional_sites.2"
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="6"] <- "not_first_course_therapy.6"
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="7"] <- "Recommended_refused.7"
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="8"] <- "Recommended_unknown.8"
  levels(reasonfornosurgery)[levels(reasonfornosurgery)=="9"] <- "Unknown.9"
  
  
    new.d <- data.frame(new.d, reasonfornosurgery)
    new.d <- apply_labels(new.d, reasonfornosurgery = "reason_for_no_surgery")
    #summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, reasonfornosurgery)
summarytools::view(dfSummary(new.d$reasonfornosurgery, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 reasonfornosurgery [labelled, factor] reason_for_no_surgery 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
1402(39.4%)
2012(56.6%)
41(1.2%)
12(0.3%)
47(1.3%)
28(0.8%)
15(0.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
155(48.3%)
146(45.5%)
4(1.2%)
0(0.0%)
9(2.8%)
6(1.9%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
61(29.0%)
143(68.1%)
2(1.0%)
0(0.0%)
4(1.9%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
168(53.3%)
138(43.8%)
1(0.3%)
1(0.3%)
5(1.6%)
1(0.3%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
160(44.9%)
178(50.0%)
8(2.2%)
4(1.1%)
5(1.4%)
1(0.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
276(47.2%)
270(46.2%)
10(1.7%)
6(1.0%)
7(1.2%)
3(0.5%)
13(2.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
575(32.8%)
1128(64.3%)
16(0.9%)
1(0.1%)
17(1.0%)
17(1.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 reason_for_no_surgery [factor] 1. Site_performed.0 2. not_part_of_planned.1 3. Other_regional_sites.2 4. not_first_course_therapy. 5. Recommended_refused.7 6. Recommended_unknown.8 7. Unknown.9
7(43.8%)
9(56.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–RADIATION

  • Description: Codes for the type of radiation therapy performed as part of the first course of treatment. Note: Radiation to brain and central nervous system for leukemia and lung cases is coded as radiation in this field.

  • Codes

    • 0 None
    • 1 Beam radiation
    • 2 Radioactive implants
    • 3 Radioisotopes
    • 4 Combination of 1 with 2 or 3
    • 5 Radiation, NOS-method or source not specified
    • 6 Currently allowable for historic cases only; see note below
    • 7 Patient or patient’s guardian refused*
    • 8 Radiation recommended, unknown if administered*
    • 9 Unknown if radiation administered

  • Note: CoC discontinued collection of this item in 2003 when FORDS was implemented. For CoC, codes 7 and 8 were used for tumors diagnosed before 1996, but should have been converted to 0 in this field and to the appropriate code in the new field Reason for No Radiation [1430]. SEER continues to use codes 7 and 8 for all years. See Chapter V, Unresolved Issues, for further discussion.

  • In the SEER program, a code 2 for other radiation was used between 1973 and 1987. When the radiation codes were expanded to add codes ‘2’ radioactive implants and ‘3’ radioisotopes, all cases with a code ‘2’ and diagnosed in 1973-1987 were converted to a code ‘6’ radiation other than beam radiation.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1360


All data

st_css() #IMPORTANT!
  rxsummradiation <- as.factor(trimws(d[,"rxsummradiation"]))
  levels(rxsummradiation) <- list(None.0="0",
                                  Beam_radiation.1="1",
                                  Radioactive_implants.2="2",
                                  Radioisotopes.3="3",
                                  Combination_12_or_13.4="4",
                                  NOS_or_source_not_specified.5="5",
                                  Historic_cases_only.6="6",
                                  Refused.7="7",
                                  Radiation_recommended_unknown.8="8",
                                  Unknown.9 = "9")
    new.d <- data.frame(new.d, rxsummradiation)
    new.d <- apply_labels(new.d, rxsummradiation = "Type of radiation therapy")
    #summary(new.d$rxsummradiation)
temp.d <- data.frame (new.d.1, rxsummradiation)
summarytools::view(dfSummary(new.d$rxsummradiation, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummradiation [labelled, factor] Type of radiation therapy 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
2095(59.6%)
853(24.3%)
219(6.2%)
8(0.2%)
232(6.6%)
5(0.1%)
0(0.0%)
14(0.4%)
57(1.6%)
34(1.0%)
 40 (1.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
228(71.0%)
66(20.6%)
8(2.5%)
0(0.0%)
3(0.9%)
0(0.0%)
0(0.0%)
4(1.2%)
10(3.1%)
2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
120(57.1%)
56(26.7%)
19(9.0%)
0(0.0%)
15(7.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
217(68.9%)
61(19.4%)
18(5.7%)
0(0.0%)
5(1.6%)
1(0.3%)
0(0.0%)
6(1.9%)
5(1.6%)
2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
206(64.6%)
87(27.3%)
6(1.9%)
0(0.0%)
13(4.1%)
2(0.6%)
0(0.0%)
0(0.0%)
2(0.6%)
3(0.9%)
 37 (10.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
346(59.2%)
185(31.7%)
12(2.1%)
0(0.0%)
12(2.1%)
0(0.0%)
0(0.0%)
2(0.3%)
1(0.2%)
26(4.5%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
967(55.2%)
394(22.5%)
155(8.8%)
8(0.5%)
184(10.5%)
2(0.1%)
0(0.0%)
2(0.1%)
39(2.2%)
1(0.1%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_radiation [factor] 1. None.0 2. Beam_radiation.1 3. Radioactive_implants.2 4. Radioisotopes.3 5. Combination_12_or_13.4 6. NOS_or_source_not_specifi 7. Historic_cases_only.6 8. Refused.7 9. Radiation_recommended_unk 10. Unknown.9
11(68.8%)
4(25.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SURG/RAD SEQ

  • Description: Codes for the sequencing of radiation and surgery given as part of the first course of treatment. See also RX Summ–Surg Prim Site [1290], RX Summ–Scope LN Surg [1292], RX Summ–Surg Oth Reg/Dis [1294], and RX Summ–Radiation [1360].

  • Codes

    • 0 No radiation and/or no surgery; unknown if surgery and/or radiation given
    • 2 Radiation before surgery
    • 3 Radiation after surgery
    • 4 Radiation both before and after surgery
    • 5 Intraoperative radiation
    • 6 Intraoperative radiation with other radiation given before and/or after surgery
    • 7 Surgery both before and after radiation
    • 9 Sequence unknown, but both surgery and radiation were given

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1380


All data

st_css() #IMPORTANT!
  rxsummsurgradseq <- as.factor(trimws(d[,"rxsummsurgradseq"]))
  levels(rxsummsurgradseq) <- list(No_radiation_surgery.0="0",
                                  Before_surgery.2="2",
                                  After_surgery.3="3",
                                  Both_before_after_surg.4="4",
                                  Intraoperative_radiation.5="5",
                                  Intraoperative_with_other.6="6",
                                  Both_before_after_radia.7="7",
                                  Unknown_Sequence.9 = "9")
    new.d <- data.frame(new.d, rxsummsurgradseq)
    new.d <- apply_labels(new.d, rxsummsurgradseq = "rx_summ_surg_rad_seq")
    #summary(new.d$rxsummsurgradseq)
temp.d <- data.frame (new.d.1, rxsummsurgradseq)
summarytools::view(dfSummary(new.d$rxsummsurgradseq, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummsurgradseq [labelled, factor] rx_summ_surg_rad_seq 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
3411(95.9%)
3(0.1%)
139(3.9%)
0(0.0%)
1(0.0%)
0(0.0%)
0(0.0%)
3(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
314(97.8%)
1(0.3%)
6(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
204(97.1%)
0(0.0%)
6(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
302(95.9%)
0(0.0%)
12(3.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
335(94.1%)
0(0.0%)
21(5.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
556(95.0%)
0(0.0%)
28(4.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
1685(96.1%)
2(0.1%)
65(3.7%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_surg_rad_seq [factor] 1. No_radiation_surgery.0 2. Before_surgery.2 3. After_surgery.3 4. Both_before_after_surg.4 5. Intraoperative_radiation. 6. Intraoperative_with_other 7. Both_before_after_radia.7 8. Unknown_Sequence.9
15(93.8%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–CHEMO

  • Description: Codes for chemotherapy given as part of the first course of treatment or the reason chemotherapy was not given. Includes treatment given at all facilities as part of the first course. Note: Prior to 2013, targeted therapies that invoke an immune response, such as Herceptin, had been coded as chemotherapy. Effective with cases diagnosed January 1, 2013, and forward these therapies are classified as biological response modifiers. Coding instructions for these changes have been added to the remarks field for the applicable drugs in the SEER*RX Interactive Drug Database ( http://seer.cancer.gov/tools/seerrx/).

  • Codes (Refer to the most recent version of STORE for additional instructions.)

    • 00 None, chemotherapy was not part of the planned first course of therapy.
    • 01 Chemotherapy, NOS.
    • 02 Chemotherapy, single agent.
    • 03 Chemotherapy, multiple agents.
    • 82 Chemotherapy was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
    • 85 Chemotherapy was not administered because the patient died prior to planned or recommended therapy.
    • 86 Chemotherapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
    • 87 Chemotherapy was not administered; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
    • 88 Chemotherapy was recommended, but it is unknown if it was administered.
    • 99 It is unknown whether a chemotherapeutic agent(s) was recommended or administered because it is not stated in patient record; death certificate-only cases.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1390


All data

st_css() #IMPORTANT!
  rxsummchemo <- as.factor(trimws(d[,"rxsummchemo"]))
  levels(rxsummchemo) <- list(None.0="0",
                                  Chemo_NOS.1="1",
                                  Chemo_single_agent.2="2",
                                  Chemo_multiple_agents.3="3",
                                  Not_recom_contraindicated.82="82",
                                  Not_admin_first_course_trp.86="86",
                                  Not_admin_refused.87="87",
                                  Unknown_if_administered.88="88",
                                  unknown.99="99")
    new.d <- data.frame(new.d, rxsummchemo)
    new.d <- apply_labels(new.d, rxsummchemo = "Chemotherapy as part of the first course of treatment")
    #summary(new.d$rxsummchemo)
temp.d <- data.frame (new.d.1, rxsummchemo)
summarytools::view(dfSummary(new.d$rxsummchemo, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummchemo [labelled, factor] Chemotherapy as part of the first course of treatment 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
2880(97.3%)
1(0.0%)
28(0.9%)
4(0.1%)
1(0.0%)
1(0.0%)
3(0.1%)
6(0.2%)
37(1.2%)
 596 (16.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
315(98.1%)
0(0.0%)
4(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
206(98.1%)
0(0.0%)
4(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
307(97.5%)
0(0.0%)
7(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
174(99.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
 181 (50.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
286(88.8%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
35(10.9%)
 263 (45.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
1576(98.4%)
1(0.1%)
12(0.7%)
4(0.2%)
1(0.1%)
0(0.0%)
2(0.1%)
6(0.4%)
0(0.0%)
 152 (8.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_chemo [factor] 1. None.0 2. Chemo_NOS.1 3. Chemo_single_agent.2 4. Chemo_multiple_agents.3 5. Not_recom_contraindicated 6. Not_admin_first_course_tr 7. Not_admin_refused.87 8. Unknown_if_administered.8 9. unknown.99
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–HORMONE

  • Description: Records whether systemic hormonal agents were administered as first-course treatment at any facility, or the reason they were not given. Hormone therapy consists of a group of drugs that may affect the long-term control of a cancer’s growth. It is not usually used as a curative measure.

  • Rationale: Systemic therapy may involve the administration of one or a combination of agents. This data item allows for the evaluation of the administration of hormonal agents as part of the first course of therapy.

  • Codes (Refer to the most recent version of STORE and the SEER Program Code Manual for additional instructions.)

    • 00 None, hormone therapy was not part of the planned first course of therapy.
    • 01 Hormone therapy administered as first course therapy.
    • 82 Hormone therapy was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
    • 85 Hormone therapy was not administered because the patient died prior to planned or recommended therapy.
    • 86 Hormone therapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
    • 87 Hormone therapy was not administered. It was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
    • 88 Hormone therapy was recommended, but it is unknown if it was administered.
    • 99 It is unknown whether a hormonal agent(s) was recommended or administered because it is not stated in the patient record. Death certificate-only cases.

  • Note: For tumors diagnosed on or after January 1, 2003, information on endocrine surgery and/or endocrine radiation should be coded in the new field, RX Summ–Transplnt/Endocr [3250]. The CoC standards for hospitals do not allow use of codes 02-03 in tumors diagnosed on or after January 1, 2003.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1400


All data

st_css() #IMPORTANT!
  rxsummhormone <- as.factor(trimws(d[,"rxsummhormone"]))
  levels(rxsummhormone) <- list(None.0="0",
                              HT_first_course.1="1",
                              Not_recom_contraindicated.82="82",
                              Not_admin_died.85="85",
                              Recom_not_admin.86="86",
                              Recomm_but_refused.87="87",
                              Unknown_if_admin.88="88",
                              Unknown.99="99")
    new.d <- data.frame(new.d, rxsummhormone)
    new.d <- apply_labels(new.d, rxsummhormone = "Hormonal agents as part of the first course of treatment")
    #summary(new.d$rxsummhormone)
temp.d <- data.frame (new.d.1, rxsummhormone)
summarytools::view(dfSummary(new.d$rxsummhormone, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummhormone [labelled, factor] Hormonal agents as part of the first course of treatment 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
2281(76.9%)
582(19.6%)
2(0.1%)
0(0.0%)
6(0.2%)
30(1.0%)
21(0.7%)
46(1.5%)
 589 (16.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
243(75.7%)
73(22.7%)
1(0.3%)
0(0.0%)
1(0.3%)
2(0.6%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
155(73.8%)
51(24.3%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.9%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
250(79.4%)
59(18.7%)
0(0.0%)
0(0.0%)
1(0.3%)
1(0.3%)
2(0.6%)
2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
129(73.7%)
43(24.6%)
1(0.6%)
0(0.0%)
0(0.0%)
1(0.6%)
1(0.6%)
0(0.0%)
 181 (50.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
205(62.5%)
80(24.4%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
4(1.2%)
37(11.3%)
 257 (43.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
1284(80.1%)
275(17.2%)
0(0.0%)
0(0.0%)
4(0.2%)
20(1.2%)
14(0.9%)
6(0.4%)
 151 (8.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_hormone [factor] 1. None.0 2. HT_first_course.1 3. Not_recom_contraindicated 4. Not_admin_died.85 5. Recom_not_admin.86 6. Recomm_but_refused.87 7. Unknown_if_admin.88 8. Unknown.99
15(93.8%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–BRM

  • Description:Records whether immunotherapeutic (biologic response modifiers) agents were administered as first-course treatment at all facilities or the reason they were not given. Immunotherapy consists of biological or chemical agents that alter the immune system or change the host’s response to tumor cells.

  • Rationale: Systemic therapy may involve the administration of one or a combination of agents. This data item allows for the evaluation of the administration of immunotherapeutic agents as part of the first course of therapy.

  • Note: Prior to 2013, targeted therapies that invoke an immune response, such as Herceptin, had been coded as chemotherapy. Effective with cases diagnosed January 1, 2013, and forward these therapies are classified as biological response modifiers. Coding instructions for these changes have been added to the remarks field for the applicable drugs in the SEER*RX Interactive Drug Database ( http://seer.cancer.gov/tools/seerrx/).

  • Codes (Refer to the most recent version of STORE and the SEER Program Code Manual for additional instructions.)

    • 00 None, immunotherapy was not part of the planned first course of therapy.
    • 01 Immunotherapy administered as first course therapy.
    • 82 Immunotherapy was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
    • 85 Immunotherapy was not administered because the patient died prior to planned or recommended therapy.
    • 86 Immunotherapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
    • 87 Immunotherapy was not administered. It was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
    • 88 Immunotherapy was recommended, but it is unknown if it was administered.
    • 99 It is unknown whether an immunotherapeutic agent(s) was recommended or administered because it is not stated in patient record; death certificate-only cases.

  • Note: For tumors diagnosed on or after January 1, 2003, information on bone marrow transplants and stem cell transplants should be coded in the new field, RX SUMM–Transplnt/Endocr [3250]. The CoC standards for hospitals do not allow use of codes 02-06 in tumors diagnosed on or after January 1, 2003.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1410


All data

st_css() #IMPORTANT!
  rxsummbrm <- as.factor(trimws(d[,"rxsummbrm"]))
  levels(rxsummbrm) <- list(None.0="0",
                              Immunotherapy_first_course.1="1",
                              Recomm_not_admin.86="86",
                              Unknown_if_admin.88="88",
                              Unknown.99="99")
    new.d <- data.frame(new.d, rxsummbrm)
    new.d <- apply_labels(new.d, rxsummbrm = "rx_summ_brm")
    #summary(new.d$rxsummbrm)
temp.d <- data.frame (new.d.1, rxsummbrm)
summarytools::view(dfSummary(new.d$rxsummbrm, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummbrm [labelled, factor] rx_summ_brm 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
2914(98.4%)
6(0.2%)
1(0.0%)
1(0.0%)
38(1.3%)
 597 (16.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
320(99.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
210(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
314(99.7%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
174(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 182 (51.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
287(88.6%)
0(0.0%)
0(0.0%)
0(0.0%)
37(11.4%)
 261 (44.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
1593(99.6%)
5(0.3%)
1(0.1%)
1(0.1%)
0(0.0%)
 154 (8.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_brm [factor] 1. None.0 2. Immunotherapy_first_cours 3. Recomm_not_admin.86 4. Unknown_if_admin.88 5. Unknown.99
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–OTHER

  • Description:Identifies other treatment given at all facilities that cannot be defined as surgery, radiation, or systemic therapy according to the defined data items in this manual. Treatment for reportable hematopoietic diseases can be supportive care, observation, or any treatment that does not meet the usual definition in which treatment modifies, controls, removes, or destroys proliferating cancer tissue. Such treatments include phlebotomy, transfusions, and aspirin.

  • Rationale: Information on other therapy is used to describe and evaluate the quality-of-care and treatment practices.

  • Codes (Refer to the most recent version of STORE and the SEER Program Code Manual for additional instructions.)

    • 0 None
    • 1 Other
    • 2 Other Experimental
    • 3 Other-Double Blind
    • 6 Other-Unproven
    • 7 Refusal
    • 8 Recommended
    • 9 Unknown; unknown if administered

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1420


All data

st_css() #IMPORTANT!
  rxsummother <- as.factor(trimws(d[,"rxsummother"]))
  levels(rxsummother) <- list(None.0="0",
                              Other.1="1",
                              Other_Unproven.86="6",
                              Unknown.9="9")
    new.d <- data.frame(new.d, rxsummother)
    new.d <- apply_labels(new.d, rxsummother = "rx_summ_other")
    #summary(new.d$rxsummother)
temp.d <- data.frame (new.d.1, rxsummother)
summarytools::view(dfSummary(new.d$rxsummother, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummother [labelled, factor] rx_summ_other 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
3519(98.9%)
3(0.1%)
1(0.0%)
34(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
321(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
210(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
314(99.7%)
0(0.0%)
1(0.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
356(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
551(94.2%)
0(0.0%)
0(0.0%)
34(5.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
1751(99.8%)
3(0.2%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_other [factor] 1. None.0 2. Other.1 3. Other_Unproven.86 4. Unknown.9
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RAD–REGIONAL DOSE: CGY

  • Description: The dominant or most clinically significant total dose of regional radiation therapy delivered to the patient during the first course of treatment. The unit of measure is centiGray (cGy). See also Rad–Regional RX Modality [1570].

  • Codes (in addition to actual doses)

    • (Fill spaces) Record the actual regional dose delivered
    • 00000 Radiation therapy was not administered
    • 88888 Not applicable, brachytherapy or radioisotopes administered to the patient
    • 99999 Regional radiation therapy was administered, but the dose is unknown

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1510


All data

st_css() #IMPORTANT!
  radregionaldosecgy <- as.factor(trimws(d[,"radregionaldosecgy"]))

    new.d <- data.frame(new.d, radregionaldosecgy)
  new.d <- apply_labels(new.d, radregionaldosecgy = "rad_regional_dose_cgy")

temp.d <- data.frame (new.d.1, radregionaldosecgy)
summarytools::view(dfSummary(new.d$radregionaldosecgy, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 radregionaldosecgy [labelled, factor] rad_regional_dose_cgy No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RAD–REGIONAL RX MODALITY

  • Description: Records the dominant modality of radiation therapy used to deliver the clinically most significant regional dose to the primary volume of interest during the first course of treatment.

  • Rationale: Radiation treatment frequently is delivered in two or more phases that can be summarized as regional and boost treatments. To evaluate patterns of radiation oncology care, it is necessary to know which radiation resources were employed in the delivery of therapy. For outcomes analysis, the modalities used for each of these phases can be very important.

  • Codes

    • 00 No radiation treatment
    • 20 External beam, NOS
    • 21 Orthovoltage
    • 22 Cobalt-60, Cesium-137
    • 23 Photons (2-5 MV)
    • 24 Photons (6-10 MV)
    • 25 Photons (11-19 MV)
    • 26 Photons (> 19 MV)
    • 27 Photons (mixed energies)
    • 28 Electrons
    • 29 Photons and electrons mixed
    • 30 Neutrons, with or without photons/electrons
    • 31 IMRT
    • 32 Conformal or 3-D therapy
    • 40 Protons
    • 41 Stereotactic radiosurgery, NOS
    • 42 Linac radiosurgery
    • 43 Gamma Knife
    • 50 Brachytherapy, NOS
    • 51 Brachytherapy, Intracavitary, Low Dose Rate (LDR)
    • 52 Brachytherapy, Intracavitary, High Dose Rate (HDR)
    • 53 Brachytherapy, Interstitial, Low Dose Rate (LDR)
    • 54 Brachytherapy, Interstitial, High Dose Rate (HDR)
    • 55 Radium
    • 60 Radio-isotopes, NOS
    • 61 Strontium - 89
    • 62 Strontium - 90
    • 80* Combination modality, specified
    • 85* Combination modality, NOS
    • 98 Other, NOS
    • 99 Unknown

  • Note: For tumors diagnosed prior to January 1, 2003, the codes reported in this data item describe any radiation administered to the patient as part or all of the first course of therapy.

  • *Codes 80 and 85 describe specific converted descriptions of radiation therapy coded according to Volume II ROADS, and DAM rules and should only be used to record regional radiation for tumors diagnosed prior to January 1, 2003.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1570


All data

st_css() #IMPORTANT!
  radregionalrxmodality <- as.factor(trimws(d[,"radregionalrxmodality"]))
  levels(radregionalrxmodality) <- list(No_tx.0="0",
                                  External_beam.20="20",
                                  Photons_6_10_MV.24="24",
                                  Photons_11_19_MV.25="25",
                                  Photons_19more_MV.26="26",
                                  Photons_mixed.27="27",
                                  IMRT.31="31",
                                  Conformal_or_3D_therapy.32="32",
                                  Protons.40="40",
                                  Stereotactic_radiosurgery.41 = "41", 
                                  Linac_radiosurgery.42 = "42",
                                  Brachytherapy_NOS.50 = "50", 
                                  Brachytherapy_Intracavitary_LDR.51 = "51", 
                                  Brachytherapy_Intracavitary_HDR.52 = "52", 
                                  Brachytherapy_Interstitial_LDR.53 = "53",
                                  Brachytherapy_Interstitial_HDR.54 = "54",
                                  Radium.55 = "55", 
                                  Radio_isotopes.60 = "60", 
                                  Combination_modality_specified.80 = "80", 
                                  Other_NOS.98 = "98", 
                                  Unknown.99 = "99"
                                  )
    new.d <- data.frame(new.d, radregionalrxmodality)
    new.d <- apply_labels(new.d, radregionalrxmodality = "rad_regional_rx_modality")
    #summary(new.d$radregionalrxmodality)
temp.d <- data.frame (new.d.1, radregionalrxmodality)
summarytools::view(dfSummary(new.d$radregionalrxmodality, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 radregionalrxmodality [labelled, factor] rad_regional_rx_modality 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
1689(57.8%)
75(2.6%)
197(6.7%)
14(0.5%)
2(0.1%)
11(0.4%)
590(20.2%)
13(0.4%)
14(0.5%)
9(0.3%)
25(0.9%)
32(1.1%)
5(0.2%)
6(0.2%)
140(4.8%)
60(2.1%)
1(0.0%)
5(0.2%)
1(0.0%)
4(0.1%)
30(1.0%)
 634 (17.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
242(75.4%)
12(3.7%)
10(3.1%)
1(0.3%)
2(0.6%)
0(0.0%)
40(12.5%)
2(0.6%)
1(0.3%)
0(0.0%)
1(0.3%)
6(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
120(57.1%)
7(3.3%)
18(8.6%)
2(1.0%)
0(0.0%)
1(0.5%)
36(17.1%)
1(0.5%)
0(0.0%)
2(1.0%)
1(0.5%)
0(0.0%)
0(0.0%)
3(1.4%)
8(3.8%)
11(5.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
228(72.4%)
6(1.9%)
17(5.4%)
2(0.6%)
0(0.0%)
1(0.3%)
36(11.4%)
2(0.6%)
2(0.6%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
1(0.3%)
8(2.5%)
7(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 355 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
190(44.8%)
7(1.7%)
54(12.7%)
6(1.4%)
0(0.0%)
3(0.7%)
118(27.8%)
0(0.0%)
6(1.4%)
2(0.5%)
0(0.0%)
6(1.4%)
1(0.2%)
1(0.2%)
3(0.7%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
25(5.9%)
 161 (27.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.3 9. Protons.40 10. Stereotactic_radiosurgery 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavita 14. Brachytherapy_Intracavita 15. Brachytherapy_Interstitia 16. Brachytherapy_Interstitia 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_spec 20. Other_NOS.98 21. Unknown.99
909(55.0%)
42(2.5%)
98(5.9%)
3(0.2%)
0(0.0%)
6(0.4%)
360(21.8%)
8(0.5%)
5(0.3%)
5(0.3%)
23(1.4%)
18(1.1%)
4(0.2%)
1(0.1%)
121(7.3%)
39(2.4%)
1(0.1%)
5(0.3%)
1(0.1%)
2(0.1%)
1(0.1%)
 102 (5.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_regional_rx_modality [factor] 1. No_tx.0 2. External_beam.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_19more_MV.26 6. Photons_mixed.27 7. IMRT.31 8. Conformal_or_3D_therapy.32 9. Protons.40 10. Stereotactic_radiosurgery.41 11. Linac_radiosurgery.42 12. Brachytherapy_NOS.50 13. Brachytherapy_Intracavitary_LDR.51 14. Brachytherapy_Intracavitary_HDR.52 15. Brachytherapy_Interstitial_LDR.53 16. Brachytherapy_Interstitial_HDR.54 17. Radium.55 18. Radio_isotopes.60 19. Combination_modality_specified.80 20. Other_NOS.98 21. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SYSTEMIC/SUR SEQ

  • Description: Records the sequencing of systemic therapy (RX Summ-Chemo [1390], RX Summ-Hormone [1400], RX Summ-BRM [1410], and RX Summ-Transplnt/Endocr [3250]) and surgical procedures given as part of the first course of treatment. See also RX Summ–Surg Prim Site [1290], RX Summ–Scope LN Surg [1292], and RX Summ–Surg Oth Reg/Dis [1294].

  • Rationale: The sequence of systemic therapy and surgical procedures given as part of the first course of treatment cannot always be determined using the date on which each modality was started or performed. This data item can be used to more precisely evaluate the time of delivery of treatment to the patient.

  • Codes

    • 0 No systemic therapy and/or surgical procedures; unknown if surgery and/or systemic therapy given
    • 2 Systemic therapy before surgery
    • 3 Systemic therapy after surgery
    • 4 Systemic therapy both before and after surgery
    • 5 Intraoperative systemic therapy
    • 6 Intraoperative systemic therapy with other therapy administered before and/or after surgery
    • 7 Surgery both before and after systemic therapy
    • 9 Sequence unknown, but both surgery and systemic therapy given

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1639


All data

st_css() #IMPORTANT!
  rxsummsystemicsurseq <- as.factor(trimws(d[,"rxsummsystemicsurseq"]))
  levels(rxsummsystemicsurseq) <- list(No.0="0",
                                  Therapy_before_surg.2="2",
                                  Therapy_after_surg.3="3",
                                  Both_before_after.4="4",
                                  Sequence_unknown_not_given.9="9"
                                  )
    new.d <- data.frame(new.d, rxsummsystemicsurseq)
    new.d <- apply_labels(new.d, rxsummsystemicsurseq = "rx_summ_systemic_sur_seq")
    #summary(new.d$rxsummsystemicsurseq)
temp.d <- data.frame (new.d.1, rxsummsystemicsurseq)
summarytools::view(dfSummary(new.d$rxsummsystemicsurseq, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummsystemicsurseq [labelled, factor] rx_summ_systemic_sur_seq 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
3438(96.7%)
20(0.6%)
93(2.6%)
5(0.1%)
1(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
302(94.1%)
7(2.2%)
11(3.4%)
1(0.3%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
204(97.1%)
2(1.0%)
4(1.9%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
301(95.6%)
4(1.3%)
7(2.2%)
3(1.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
339(95.2%)
1(0.3%)
16(4.5%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
568(97.1%)
2(0.3%)
14(2.4%)
0(0.0%)
1(0.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
1708(97.4%)
4(0.2%)
41(2.3%)
1(0.1%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_summ_systemic_sur_seq [factor] 1. No.0 2. Therapy_before_surg.2 3. Therapy_after_surg.3 4. Both_before_after.4 5. Sequence_unknown_not_give
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SURG SITE 98-02

  • Description: Site-specific codes for the type of surgery to the primary site performed as part of the first course of treatment. This includes treatment given at all facilities as part of the first course of treatment. This field is to be used for ROADS codes after the ROADS to FORDS conversion. It is also to be used to code Surgery Primary Site at all facilities for all tumors diagnosed before January 1, 2003.
  • Rationale: If central registries wish to study the treatment given at particular hospitals, the hospital-level treatment fields must be used.
  • Codes (in addition to the site-specific codes)
    • 00 No primary site surgery performed
    • 99 Unknown if primary site surgery performed
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1646


All data

st_css() #IMPORTANT!
  rxsummsurgsite9802 <- as.factor(trimws(d[,"rxsummsurgsite9802"]))

    new.d <- data.frame(new.d, rxsummsurgsite9802)
  new.d <- apply_labels(new.d, rxsummsurgsite9802 = "rx_summ_surg_site_9802")

temp.d <- data.frame (new.d.1, rxsummsurgsite9802)
summarytools::view(dfSummary(new.d$rxsummsurgsite9802, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummsurgsite9802 [labelled, factor] rx_summ_surg_site_9802 No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SCOPE REG 98-02

  • Description: Describes the removal, biopsy or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event at all facilities. This field is to be used for ROADS codes after the ROADS to FORDS conversion. It is also to be used to code Scope of Regional Lymph Node Surgery at all facilities for all tumors diagnosed before January 1, 2003.
  • Rationale: In evaluating quality of care and treatment practices it is important to identify the removal, biopsy, or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event.
  • Codes (See the CoC ROADS Manual 1998 Supplement and the SEER Program Code Manual for site-specific codes.)
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1647


All data

st_css() #IMPORTANT!
  rxsummscopereg9802 <- as.factor(trimws(d[,"rxsummscopereg9802"]))

    new.d <- data.frame(new.d, rxsummscopereg9802)
  new.d <- apply_labels(new.d, rxsummscopereg9802 = "rx_summ_scope_reg_9802")

temp.d <- data.frame (new.d.1, rxsummscopereg9802)
summarytools::view(dfSummary(new.d$rxsummscopereg9802, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummscopereg9802 [labelled, factor] rx_summ_scope_reg_9802 No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–SCOPE OTH 98-02

  • Description: Records the surgical removal of distant lymph nodes or other tissue(s)/organ(s) beyond the primary site given at all facilities as part of the first course of treatment. This field is to be used for ROADS codes after the ROADS to FORDS conversion. It is also to be used to code Surgery Regional/Distant Sites at all facilities for all tumors diagnosed before January 1, 2003.
  • Rationale: The removal of non-primary tissue documents the extent of surgical treatment and is useful in evaluating the extent of metastatic involvement.
  • Codes (See the CoC ROADS Manual 1998 Supplement and the SEER Program Code Manual for site-specific codes.)
  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1648


All data

st_css() #IMPORTANT!
  rxsummsurgoth9802 <- as.factor(trimws(d[,"rxsummsurgoth9802"]))

    new.d <- data.frame(new.d, rxsummsurgoth9802)
  new.d <- apply_labels(new.d, rxsummsurgoth9802 = "rx_summ_surg_oth_9802")

temp.d <- data.frame (new.d.1, rxsummsurgoth9802)
summarytools::view(dfSummary(new.d$rxsummsurgoth9802, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummsurgoth9802 [labelled, factor] rx_summ_surg_oth_9802 No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DATE OF LAST CONTACT

  • Description: Date of last contact with the patient, or date of death. If the patient has multiple tumors, Date of Last Contact should be the same for all tumors. See Chapter X for date format.

  • Rationale: Used for recording Date of Last Contact from active or passive follow-up. Used to record date of death and to calculate survival.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1750


All data

  dateoflastcontact <- trimws(d[,"dateoflastcontact"])
  #new.d.n <- data.frame(new.d.n, dateoflastcontact) # keep NAACCR coding
  
  select99 <- ifelse(is.na(dateoflastcontact), F, substr(dateoflastcontact, start=7, stop=8)=="99")
  dateoflastcontact[select99] <- substr(dateoflastcontact[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(dateoflastcontact), F, nchar(trimws(dateoflastcontact))==6)
  dateoflastcontact[select6] <- paste(dateoflastcontact[select6], "15", sep="")
  
  select4 <- ifelse(is.na(dateoflastcontact), F, nchar(trimws(dateoflastcontact))==4)
  dateoflastcontact[select4] <- paste(dateoflastcontact[select4], "0615", sep="")
  
  dateoflastcontact <- as.Date(dateoflastcontact, c("%Y%m%d"))
  new.d <- data.frame(new.d, dateoflastcontact)
  new.d <- apply_labels(new.d, dateoflastcontact = "date_of_last_contact")
  temp.d <- data.frame (new.d.1, dateoflastcontact)
summarytools::view(dfSummary(new.d$dateoflastcontact, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 dateoflastcontact [labelled, Date] date_of_last_contact min : 2014-04-15 med : 2020-01-06 max : 2021-05-15 range : 7y 1m 0d 354 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] 1. 2016-11-15 2. 2017-01-15 3. 2017-03-15 4. 2017-08-15 5. 2017-12-15 6. 2018-01-15 7. 2018-03-15 8. 2018-04-15 9. 2018-06-15 10. 2018-11-15 11. 2018-12-15 12. 2019-01-15 13. 2019-02-15 14. 2019-03-15 15. 2019-04-15 16. 2019-05-15 17. 2019-06-15 18. 2019-07-15 19. 2019-08-15 20. 2019-09-15 21. 2019-10-15 22. 2019-11-15 23. 2019-12-15 24. 2020-01-15 25. 2020-02-15 26. 2020-03-15 27. 2020-04-15 28. 2020-05-15 29. 2020-06-15 30. 2020-07-15 31. 2020-08-15 32. 2020-10-15
5(1.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
3(0.9%)
1(0.3%)
1(0.3%)
1(0.3%)
8(2.5%)
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
3(0.9%)
3(0.9%)
2(0.6%)
1(0.3%)
1(0.3%)
2(0.6%)
4(1.2%)
5(1.6%)
2(0.6%)
1(0.3%)
3(0.9%)
234(72.9%)
3(0.9%)
5(1.6%)
2(0.6%)
13(4.0%)
8(2.5%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] min : 2017-03-09 med : 2019-12-12 max : 2020-09-15 range : 3y 6m 6d 137 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] 1. 2016-05-15 2. 2016-06-15 3. 2016-11-15 4. 2017-01-15 5. 2017-02-15 6. 2017-03-15 7. 2017-04-15 8. 2017-06-15 9. 2017-08-15 10. 2017-10-15 11. 2017-11-15 12. 2018-01-15 13. 2018-03-15 14. 2018-04-15 15. 2018-05-15 16. 2018-06-15 17. 2018-07-15 18. 2018-08-15 19. 2018-09-15 20. 2018-10-15 21. 2018-11-15 22. 2018-12-15 23. 2019-01-15 24. 2019-02-15 25. 2019-03-15 26. 2019-04-15 27. 2019-05-15 28. 2019-06-15 29. 2019-07-15 30. 2019-08-15 31. 2019-09-15 32. 2019-10-15 33. 2019-11-15 34. 2019-12-15 35. 2020-01-15 36. 2020-02-15 37. 2020-03-15 38. 2020-04-15 39. 2020-05-15 40. 2020-06-15 41. 2020-07-15 42. 2020-08-15 43. 2020-09-15 44. 2020-10-15
1(0.3%)
1(0.3%)
3(1.0%)
3(1.0%)
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
9(2.9%)
3(1.0%)
5(1.6%)
6(1.9%)
9(2.9%)
1(0.3%)
3(1.0%)
3(1.0%)
4(1.3%)
27(8.6%)
6(1.9%)
7(2.2%)
22(7.0%)
5(1.6%)
7(2.2%)
9(2.9%)
11(3.5%)
13(4.1%)
10(3.2%)
9(2.9%)
10(3.2%)
13(4.1%)
5(1.6%)
50(15.9%)
6(1.9%)
7(2.2%)
4(1.3%)
7(2.2%)
6(1.9%)
7(2.2%)
7(2.2%)
7(2.2%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] 1. 2014-04-15 2. 2016-05-15 3. 2016-06-15 4. 2016-11-15 5. 2018-08-15 6. 2018-09-15 7. 2018-11-15 8. 2019-01-15 9. 2019-02-15 10. 2019-03-15 11. 2019-04-15 12. 2019-05-15 13. 2019-07-15 14. 2019-08-15 15. 2019-10-15 16. 2019-12-15 17. 2020-01-15 18. 2020-02-15 19. 2020-03-15 20. 2020-04-15 21. 2020-05-15 22. 2020-06-15 23. 2020-07-15 24. 2020-08-15 25. 2020-09-15 26. 2020-11-15 27. 2020-12-15 28. 2021-01-15 29. 2021-02-15 30. 2021-03-15 31. 2021-04-15 32. 2021-05-15
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
3(0.8%)
4(1.1%)
2(0.6%)
6(1.7%)
5(1.4%)
1(0.3%)
1(0.3%)
1(0.3%)
2(0.6%)
1(0.3%)
2(0.6%)
1(0.3%)
136(38.2%)
1(0.3%)
5(1.4%)
6(1.7%)
6(1.7%)
7(2.0%)
8(2.2%)
3(0.8%)
2(0.6%)
1(0.3%)
2(0.6%)
132(37.1%)
6(1.7%)
5(1.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] min : 2015-09-15 med : 2020-01-03 max : 2021-04-19 range : 5y 7m 4d 56 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] min : 2015-03-23 med : 2020-01-06 max : 2021-03-16 range : 5y 11m 21d 152 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 date_of_last_contact [Date] 1. 2020-03-15 2. 2020-08-15
15(93.8%)
1(6.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


VITAL STATUS


All data

st_css() #IMPORTANT!
  vitalstatus <- as.factor(trimws(d[,"vitalstatus"]))
  levels(vitalstatus) <- list(Dead.0="0",
                              Alive.1="1")
    new.d <- data.frame(new.d, vitalstatus)
    new.d <- apply_labels(new.d, vitalstatus = "Vital status of the patient")
    #summary(new.d$vitalstatus)
temp.d <- data.frame (new.d.1, vitalstatus)
summarytools::view(dfSummary(new.d$vitalstatus, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 vitalstatus [labelled, factor] Vital status of the patient 1. Dead.0 2. Alive.1
11(0.3%)
3546(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
1(0.3%)
320(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
2(1.0%)
208(99.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
0(0.0%)
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
3(0.8%)
353(99.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
0(0.0%)
585(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
5(0.3%)
1749(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 vital_status [factor] 1. Dead.0 2. Alive.1
0(0.0%)
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SURV-DATE ACTIVE FOLLOWUP

  • Description: The Surv-Date Active Followup is defined as the earlier of the Date of Last Contact [1750] and a study cutoff date. The study cut-off date is a pre-determined date based on the year of data submission and is set in the survival program used to derive the seven survival variables. If the Date of Last Contact [1750] is earlier than the study cut-off date and either the day or month is unknown or not available, the values are imputed by the survival program. The survival program is available from your standard setter or NAACCR.

Example 1 Date of Last Contact: 20111120 Study Cut-off Date: 20111231 Surv-Date Active Followup: 20111120 Note: The date of last contact is earlier than the study cut-off date, and the date of last contact is complete, so the date of last contact is used in Surv-Date Active Followup.

Example 2 Date of Last Contact: 201111 Study Cut-off Date: 20111231 Surv-Date Active Followup: 20111115 Note: Rationale is to take mid-point of possible values. For Nov (30 days) it would be FLOOR((1+30)/2) = 15, where FLOOR is a function that rounds a decimal down to an integer.

  • Rationale: The Surv-Date Active Followup is needed to be able to recalculate survival months if a different study cut-off date is used and provides flexibility to recalculate survival without needing to rerun the survival program on the original data. Additional information about the survival algorithm and what specific values are assigned in given missing date situations are available here: http://seer.cancer.gov/survivaltime/.

  • Codes: If Date of Last Contact [1750] is blank, Surv-Date Active Followup will also be blank.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1782


All data

  survdateactivefollowup <- trimws(d[,"survdateactivefollowup"])
  #new.d.n <- data.frame(new.d.n, survdateactivefollowup) # keep NAACCR coding
  
  select99 <- ifelse(is.na(survdateactivefollowup), F, substr(survdateactivefollowup, start=7, stop=8)=="99")
  survdateactivefollowup[select99] <- substr(survdateactivefollowup[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(survdateactivefollowup), F, nchar(trimws(survdateactivefollowup))==6)
  survdateactivefollowup[select6] <- paste(survdateactivefollowup[select6], "15", sep="")
  
  select4 <- ifelse(is.na(survdateactivefollowup), F, nchar(trimws(survdateactivefollowup))==4)
  survdateactivefollowup[select4] <- paste(survdateactivefollowup[select4], "0615", sep="")
  
  survdateactivefollowup <- as.Date(survdateactivefollowup, c("%Y%m%d"))
  new.d <- data.frame(new.d, survdateactivefollowup)
  new.d <- apply_labels(new.d, survdateactivefollowup = "survdate_active_follow_up")
  temp.d <- data.frame (new.d.1, survdateactivefollowup)
summarytools::view(dfSummary(new.d$survdateactivefollowup, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 survdateactivefollowup [labelled, Date] survdate_active_follow_up min : 2014-04-15 med : 2018-12-31 max : 2019-12-31 range : 5y 8m 16d 148 distinct values 315 (8.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] 1. 2016-11-15 2. 2017-01-15 3. 2017-03-15 4. 2017-08-15 5. 2017-12-15 6. 2018-01-15 7. 2018-03-15 8. 2018-04-15 9. 2018-06-15 10. 2018-11-15 11. 2018-12-15 12. 2019-01-15 13. 2019-02-15 14. 2019-03-15 15. 2019-04-15 16. 2019-05-15 17. 2019-06-15 18. 2019-07-15 19. 2019-08-15 20. 2019-09-15 21. 2019-10-15 22. 2019-11-15 23. 2019-12-15
5(1.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
3(0.9%)
1(0.3%)
1(0.3%)
1(0.3%)
8(2.5%)
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
3(0.9%)
3(0.9%)
2(0.6%)
1(0.3%)
1(0.3%)
2(0.6%)
4(1.2%)
5(1.6%)
272(84.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] 1. 2017-03-09 2. 2017-04-14 3. 2017-07-27 4. 2018-01-09 5. 2018-02-12 6. 2018-03-07 7. 2018-05-09 8. 2018-06-19 9. 2018-08-17 10. 2018-09-08 11. 2018-10-01 12. 2018-10-05 13. 2018-10-16 14. 2018-10-25 15. 2018-11-06 16. 2018-11-07 17. 2018-12-19 18. 2018-12-31
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
2(1.0%)
1(0.5%)
5(2.4%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
14(6.7%)
1(0.5%)
1(0.5%)
175(83.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] 1. 2014-04-15 2. 2016-05-15 3. 2016-06-15 4. 2016-11-15 5. 2018-08-15 6. 2018-09-15 7. 2018-11-15 8. 2018-12-15 9. 2019-01-15 10. 2019-03-15 11. 2019-04-15 12. 2019-05-15 13. 2019-07-15 14. 2019-12-15
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
3(0.8%)
171(48.0%)
1(0.3%)
3(0.8%)
3(0.8%)
1(0.3%)
1(0.3%)
166(46.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] 1. 2015-09-15 2. 2016-03-22 3. 2016-05-19 4. 2016-07-08 5. 2016-11-11 6. 2016-12-08 7. 2017-01-10 8. 2017-01-11 9. 2017-07-13 10. 2017-07-24 11. 2017-08-21 12. 2017-09-11 13. 2018-03-06 14. 2018-05-07 15. 2018-09-06 16. 2018-11-30 17. 2018-12-14 18. 2018-12-27 19. 2018-12-31 20. 2019-01-17 21. 2019-01-30 22. 2019-02-28 23. 2019-05-02 24. 2019-07-24 25. 2019-09-20 26. 2019-10-19 27. 2019-12-02 28. 2019-12-16 29. 2019-12-31
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
283(48.4%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.3%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
273(46.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] min : 2015-03-23 med : 2018-12-31 max : 2019-12-31 range : 4y 9m 8d 77 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_active_follow_up [Date] 1. 2018-12-15
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SURV-DATE PRESUMED ALIVE

  • Description: The Surv-Date Presumed Alive is the last date for which complete death ascertainment is available from the registry at the time a file is transmitted. Because not all central cancer registries conduct active patient follow-up, it is necessary to have an option for calculating survival times based on the assumption that the registry has ascertained all available deaths (state/province and national), and persons not known to be deceased are presumed to be alive as of the last date for which complete death ascertainment is available. This variable is set in the survival program used to derive the seven survival variables. The survival program is available from your standard setter or NAACCR.

Example 1 Vital Status: Alive Date of Last Contact: 20111120 Study Cut-off Date: 20111231 Latest date for complete death ascertainment: 20111231 Surv-Date Presumed Alive: 20111231

  • Rationale: The Surv-Date Presumed Alive is needed to be able to recalculate survival months if a different study cut-off date is used and provides flexibility to recalculate survival without needing to rerun the survival program on the original data.

  • Additional information about the survival algorithm and what specific values are assigned in given missing date situations are available here: http://seer.cancer.gov/survivaltime/.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1785


All data

  survdatepresumedalive <- trimws(d[,"survdatepresumedalive"])
  #new.d.n <- data.frame(new.d.n, survdatepresumedalive) # keep NAACCR coding
  
  select99 <- ifelse(is.na(survdatepresumedalive), F, substr(survdatepresumedalive, start=7, stop=8)=="99")
  survdatepresumedalive[select99] <- substr(survdatepresumedalive[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(survdatepresumedalive), F, nchar(trimws(survdatepresumedalive))==6)
  survdatepresumedalive[select6] <- paste(survdatepresumedalive[select6], "15", sep="")
  
  select4 <- ifelse(is.na(survdatepresumedalive), F, nchar(trimws(survdatepresumedalive))==4)
  survdatepresumedalive[select4] <- paste(survdatepresumedalive[select4], "0615", sep="")
  
  survdatepresumedalive <- as.Date(survdatepresumedalive, c("%Y%m%d"))
  new.d <- data.frame(new.d, survdatepresumedalive)
  new.d <- apply_labels(new.d, survdatepresumedalive = "survdate_presumed_alive")
  temp.d <- data.frame (new.d.1, survdatepresumedalive)
summarytools::view(dfSummary(new.d$survdatepresumedalive, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 survdatepresumedalive [labelled, Date] survdate_presumed_alive 1. 2018-12-15 2. 2018-12-31 3. 2019-11-15 4. 2019-12-15 5. 2019-12-31
190(5.9%)
2110(65.1%)
1(0.0%)
502(15.5%)
439(13.5%)
 315 (8.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] 1. 2019-11-15 2. 2019-12-15
1(0.3%)
320(99.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] 1. 2018-12-31
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] 1. 2018-12-15 2. 2019-12-15
174(48.9%)
182(51.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] 1. 2018-12-31 2. 2019-12-31
298(50.9%)
287(49.1%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] 1. 2018-12-31 2. 2019-12-31
1602(91.3%)
152(8.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_presumed_alive [Date] 1. 2018-12-15
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SURV-DATE RX RECODE

  • Description: The survival date of diagnosis recode is calculated using the month, day, and year of the Date of Diagnosis [390]. If the Date of Diagnosis [390] has complete month and day information, the Surv-Date Dx Recode will be the same as the Date of Diagnosis [390]. If the day or month is unknown or not available, the values are imputed by the survival program used to derive the seven survival variables. The survival program is available from your standard setter or NAACCR.

Example 1 Date of diagnosis: 20111199 Date of Last Contact: 20111120 Surv-Date of DX Recode: 20111110 Note: The recoded value is the mid-point between 11/1 and 11/20.

Example 2 Date of diagnosis: 2011 Date of Last Contact: 20111120 Surv-Date of DX Recode: 20110611 Note: The recoded value is the mid-point between 20110101 and 20111120.

  • Rationale: The Surv-Date DX Recode is needed to be able to match to a lifetable entry to obtain expected survival. If a case is diagnosed in January 2000, the first 12 months of expected survival will be from the 2000 life table. If a case is diagnosed in December 2000, only one month will be from the 2000 life table and then the 2001 life table is used.

  • Additional information about the survival algorithm and what specific values are assigned in given missing date situations are available here: http://seer.cancer.gov/survivaltime/.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1788


All data

  survdatedxrecode <- trimws(d[,"survdatedxrecode"])
  #new.d.n <- data.frame(new.d.n, survdatedxrecode) # keep NAACCR coding
  
  select99 <- ifelse(is.na(survdatedxrecode), F, substr(survdatedxrecode, start=7, stop=8)=="99")
  survdatedxrecode[select99] <- substr(survdatedxrecode[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(survdatedxrecode), F, nchar(trimws(survdatedxrecode))==6)
  survdatedxrecode[select6] <- paste(survdatedxrecode[select6], "15", sep="")
  
  select4 <- ifelse(is.na(survdatedxrecode), F, nchar(trimws(survdatedxrecode))==4)
  survdatedxrecode[select4] <- paste(survdatedxrecode[select4], "0615", sep="")
  
  survdatedxrecode <- as.Date(survdatedxrecode, c("%Y%m%d"))
  new.d <- data.frame(new.d, survdatedxrecode)
  new.d <- apply_labels(new.d, survdatedxrecode = "survdate_dx_recode")
  temp.d <- data.frame (new.d.1, survdatedxrecode)
summarytools::view(dfSummary(new.d$survdatedxrecode, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 survdatedxrecode [labelled, Date] survdate_dx_recode min : 2011-08-15 med : 2016-04-15 max : 2018-12-18 range : 7y 4m 3d 742 distinct values 315 (8.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] 1. 2015-01-15 2. 2015-02-15 3. 2015-03-15 4. 2015-04-15 5. 2015-05-15 6. 2015-06-15 7. 2015-07-15 8. 2015-08-15 9. 2015-09-15 10. 2015-10-15 11. 2015-11-15 12. 2015-12-15 13. 2016-01-15 14. 2016-02-15 15. 2016-03-15 16. 2016-04-15 17. 2016-05-15 18. 2016-06-15 19. 2016-07-15 20. 2016-08-15 21. 2016-09-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-01-15 26. 2017-02-15 27. 2017-03-15 28. 2017-04-15 29. 2017-05-15 30. 2017-06-15 31. 2017-07-15 32. 2017-08-15 33. 2017-09-15 34. 2017-10-15 35. 2017-11-15 36. 2017-12-15
8(2.5%)
4(1.2%)
9(2.8%)
9(2.8%)
7(2.2%)
7(2.2%)
12(3.7%)
13(4.0%)
8(2.5%)
4(1.2%)
9(2.8%)
12(3.7%)
11(3.4%)
8(2.5%)
18(5.6%)
22(6.9%)
13(4.0%)
16(5.0%)
11(3.4%)
18(5.6%)
12(3.7%)
9(2.8%)
7(2.2%)
11(3.4%)
2(0.6%)
4(1.2%)
6(1.9%)
5(1.6%)
3(0.9%)
7(2.2%)
7(2.2%)
7(2.2%)
4(1.2%)
2(0.6%)
7(2.2%)
9(2.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] min : 2015-01-05 med : 2016-03-16 max : 2017-12-21 range : 2y 11m 16d 177 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] min : 2012-01-15 med : 2016-07-15 max : 2018-12-15 range : 6y 11m 0d 75 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] min : 2015-01-01 med : 2016-07-16 max : 2018-12-18 range : 3y 11m 17d 374 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] min : 2015-01-01 med : 2016-03-14 max : 2018-09-26 range : 3y 8m 25d 604 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 survdate_dx_recode [Date] 1. 2011-08-15 2. 2012-03-15 3. 2012-04-15 4. 2012-06-15 5. 2013-01-15 6. 2013-03-15 7. 2013-04-15 8. 2013-06-15 9. 2013-10-15 10. 2014-02-15 11. 2014-03-15 12. 2014-06-15 13. 2014-07-15 14. 2014-12-15 15. 2015-04-15
1(6.2%)
1(6.2%)
2(12.5%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


# CAUSE OF DEATH {.tabset} - Description: Official cause of death as coded from the death certificate in valid ICD-7, ICD-8, ICD-9, and ICD-10 codes. - Rationale: Cause of death is used for calculation of adjusted survival rates by the life table method. The adjustment corrects for deaths other than from the diagnosed cancer. - Note: This data item is no longer supported by CoC (as of January 1, 2003). - Special codes in addition to ICD-7, ICD-8, ICD-9, and ICD-10 (refer to SEER Program Code Manual for additional instructions.) + 0000 Patient alive at last contact + 7777 State death certificate not available + 7797 State death certificate available but underlying cause of death is not coded


All data

st_css() #IMPORTANT!
  causeofdeath <- as.factor(trimws(d[,"causeofdeath"]))
  levels(causeofdeath) <- list(Alive_last_contact.0="0",
                                  Certificate_not_available.7777="7777"
                                )
    new.d <- data.frame(new.d, causeofdeath)
    new.d <- apply_labels(new.d, causeofdeath = "cause_of_death")
    #summary(new.d$causeofdeath)
temp.d <- data.frame (new.d.1, causeofdeath)
summarytools::view(dfSummary(new.d$causeofdeath, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 causeofdeath [labelled, factor] cause_of_death 1. Alive_last_contact.0 2. Certificate_not_available
2925(99.9%)
2(0.1%)
 630 (17.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
320(100.0%)
0(0.0%)
 1 (0.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
208(99.0%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
315(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
171(100.0%)
0(0.0%)
 185 (52.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
298(100.0%)
0(0.0%)
 287 (49.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
1597(100.0%)
0(0.0%)
 157 (9.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cause_of_death [factor] 1. Alive_last_contact.0 2. Certificate_not_available
16(100.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS EXTENSION

  • Description: Identifies contiguous growth (extension) of the primary tumor within the organ of origin or its direct extension into neighboring organs. For certain sites such as ovary, discontinuous metastasis is coded in CS Extension.

  • Rationale: Tumor extension at diagnosis is a prognostic indicator used by Collaborative Staging to derive some TNM-T codes and some SEER Summary Stage codes.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)

  • Note: For cases diagnosed prior to 2010, this was a 2 character field in CS version 1 which was converted to a 3 character field in CS version 2. Most 2 character codes were converted by adding a zero as the third character. For example, code 05 was usually converted to 050, 10 to 100, 11 to 110, etc. Special codes such as 88 and 99 were usually converted to 888 and 999, respectively.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2810


All data

st_css() #IMPORTANT!
  csextension <- as.factor(trimws(d[,"csextension"]))
  new.d <- data.frame(new.d, csextension)
  new.d <- apply_labels(new.d, csextension = "cs_extension")
temp.d <- data.frame (new.d.1, csextension)
summarytools::view(dfSummary(new.d$csextension, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 csextension [labelled, factor] cs_extension 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
3(0.1%)
17(0.7%)
8(0.3%)
1588(67.5%)
29(1.2%)
71(3.0%)
40(1.7%)
70(3.0%)
29(1.2%)
356(15.1%)
10(0.4%)
6(0.3%)
1(0.0%)
3(0.1%)
10(0.4%)
25(1.1%)
6(0.3%)
7(0.3%)
2(0.1%)
73(3.1%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
1(1.0%)
1(1.0%)
0(0.0%)
71(69.6%)
4(3.9%)
6(5.9%)
3(2.9%)
4(3.9%)
1(1.0%)
6(5.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(2.0%)
1(1.0%)
0(0.0%)
1(1.0%)
0(0.0%)
1(1.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
1(1.1%)
0(0.0%)
1(1.1%)
59(67.0%)
1(1.1%)
9(10.2%)
6(6.8%)
1(1.1%)
2(2.3%)
5(5.7%)
1(1.1%)
0(0.0%)
1(1.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
0(0.0%)
2(1.4%)
1(0.7%)
99(70.2%)
4(2.8%)
9(6.4%)
4(2.8%)
4(2.8%)
3(2.1%)
8(5.7%)
1(0.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(2.8%)
1(0.7%)
0(0.0%)
0(0.0%)
1(0.7%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
0(0.0%)
2(1.5%)
0(0.0%)
88(65.2%)
2(1.5%)
4(3.0%)
4(3.0%)
4(3.0%)
2(1.5%)
23(17.0%)
1(0.7%)
1(0.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.7%)
1(0.7%)
1(0.7%)
0(0.0%)
1(0.7%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
0(0.0%)
1(0.8%)
1(0.8%)
87(72.5%)
5(4.2%)
2(1.7%)
3(2.5%)
1(0.8%)
7(5.8%)
9(7.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.8%)
1(0.8%)
0(0.0%)
0(0.0%)
1(0.8%)
1(0.8%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
1(0.1%)
11(0.6%)
5(0.3%)
1174(67.0%)
13(0.7%)
41(2.3%)
20(1.1%)
56(3.2%)
14(0.8%)
299(17.1%)
7(0.4%)
5(0.3%)
0(0.0%)
3(0.2%)
7(0.4%)
18(1.0%)
4(0.2%)
5(0.3%)
1(0.1%)
68(3.9%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_extension [factor] 1. 100 2. 130 3. 140 4. 150 5. 200 6. 210 7. 220 8. 230 9. 240 10. 300 11. 410 12. 420 13. 430 14. 440 15. 445 16. 450 17. 490 18. 500 19. 600 20. 999
0(0.0%)
0(0.0%)
0(0.0%)
10(62.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(37.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS LYMPH NODES

  • Description: Identifies the regional lymph nodes involved with cancer at the time of diagnosis.

  • Rationale: The involvement of specific regional lymph nodes is a prognostic indicator used by Collaborative Staging to derive some TNM-N codes and SEER Summary Stage codes.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)

  • Note: For cases prior to 2010, this was a 2 character field in CS version 1 which was converted to a 3 character field in CS version 2. Most 2 character codes were converted by adding a zero as the third character. For example, code 05 was usually converted to 050, 10 to 100, 11 to 110, etc. Special codes such as 88 and 99 were usually converted to 888 and 999 respectively.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2830


All data

st_css() #IMPORTANT!
  cslymphnodes <- as.factor(trimws(d[,"cslymphnodes"]))
  new.d <- data.frame(new.d, cslymphnodes)
  new.d <- apply_labels(new.d, cslymphnodes = "cs_lymph_nodes")
temp.d <- data.frame (new.d.1, cslymphnodes)
summarytools::view(dfSummary(new.d$cslymphnodes, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cslymphnodes [labelled, factor] cs_lymph_nodes 1. 0 2. 000 3. 100 4. 800 5. 999
2004(85.1%)
217(9.2%)
76(3.2%)
1(0.0%)
56(2.4%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
94(92.2%)
0(0.0%)
8(7.8%)
0(0.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
85(96.6%)
0(0.0%)
2(2.3%)
0(0.0%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
127(90.1%)
0(0.0%)
11(7.8%)
0(0.0%)
3(2.1%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
57(42.2%)
71(52.6%)
5(3.7%)
0(0.0%)
2(1.5%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
111(92.5%)
3(2.5%)
5(4.2%)
0(0.0%)
1(0.8%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
1514(86.4%)
143(8.2%)
45(2.6%)
1(0.1%)
49(2.8%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_lymph_nodes [factor] 1. 0 2. 000 3. 100 4. 800 5. 999
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS METS AT DX

  • Description: Identifies the distant site(s) of metastatic involvement at time of diagnosis.

  • Rationale: The presence of metastatic disease at diagnosis is an independent prognostic indicator, and it is used by Collaborative Staging to derive TNM-M codes and SEER Summary Stage codes.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2850


All data

st_css() #IMPORTANT!
  csmetsatdx <- as.factor(trimws(d[,"csmetsatdx"]))
  new.d <- data.frame(new.d, csmetsatdx)
  new.d <- apply_labels(new.d, csmetsatdx = "cs_mets_at_dx")
temp.d <- data.frame (new.d.1, csmetsatdx)
summarytools::view(dfSummary(new.d$csmetsatdx, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 csmetsatdx [labelled, factor] cs_mets_at_dx 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
2045(86.9%)
221(9.4%)
1(0.0%)
9(0.4%)
28(1.2%)
4(0.2%)
2(0.1%)
2(0.1%)
1(0.0%)
1(0.0%)
40(1.7%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
100(98.0%)
0(0.0%)
0(0.0%)
1(1.0%)
0(0.0%)
1(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
86(97.7%)
0(0.0%)
1(1.1%)
0(0.0%)
1(1.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
136(96.5%)
0(0.0%)
0(0.0%)
0(0.0%)
3(2.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.4%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
56(41.5%)
73(54.1%)
0(0.0%)
1(0.7%)
5(3.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
115(95.8%)
3(2.5%)
0(0.0%)
0(0.0%)
2(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
1536(87.7%)
145(8.3%)
0(0.0%)
7(0.4%)
17(1.0%)
3(0.2%)
2(0.1%)
2(0.1%)
1(0.1%)
1(0.1%)
38(2.2%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_mets_at_dx [factor] 1. 0 2. 00 3. 11 4. 12 5. 30 6. 35 7. 38 8. 40 9. 55 10. 60 11. 99
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 7


All data

st_css() #IMPORTANT!
  cssitespecificfactor7 <- as.factor(trimws(d[,"cssitespecificfactor7"]))
  new.d <- data.frame(new.d, cssitespecificfactor7)
  new.d <- apply_labels(new.d, cssitespecificfactor7 = "cs_site_specific_factor7")
temp.d <- data.frame (new.d.1, cssitespecificfactor7)
summarytools::view(dfSummary(new.d$cssitespecificfactor7, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor7 [labelled, factor] cs_site_specific_factor7 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
218(6.2%)
177(5.1%)
100(2.9%)
49(1.4%)
20(0.6%)
1(0.0%)
4(0.1%)
1(0.0%)
3(0.1%)
2(0.1%)
4(0.1%)
847(24.3%)
834(23.9%)
34(1.0%)
529(15.1%)
322(9.2%)
159(4.6%)
8(0.2%)
54(1.5%)
21(0.6%)
35(1.0%)
12(0.3%)
58(1.7%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
111(34.6%)
77(24.0%)
3(0.9%)
59(18.4%)
23(7.2%)
23(7.2%)
1(0.3%)
7(2.2%)
2(0.6%)
9(2.8%)
1(0.3%)
5(1.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
76(36.2%)
71(33.8%)
2(1.0%)
34(16.2%)
10(4.8%)
9(4.3%)
1(0.5%)
2(1.0%)
2(1.0%)
0(0.0%)
1(0.5%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
98(31.1%)
90(28.6%)
2(0.6%)
45(14.3%)
38(12.1%)
20(6.3%)
0(0.0%)
4(1.3%)
3(1.0%)
6(1.9%)
2(0.6%)
6(1.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
40(13.6%)
61(20.7%)
37(12.5%)
6(2.0%)
6(2.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
23(7.8%)
39(13.2%)
0(0.0%)
39(13.2%)
28(9.5%)
7(2.4%)
1(0.3%)
3(1.0%)
0(0.0%)
1(0.3%)
1(0.3%)
1(0.3%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
123(21.1%)
79(13.5%)
41(7.0%)
27(4.6%)
7(1.2%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
46(7.9%)
77(13.2%)
12(2.1%)
73(12.5%)
51(8.7%)
23(3.9%)
0(0.0%)
9(1.5%)
4(0.7%)
3(0.5%)
0(0.0%)
8(1.4%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
55(3.1%)
37(2.1%)
22(1.3%)
16(0.9%)
7(0.4%)
1(0.1%)
2(0.1%)
1(0.1%)
2(0.1%)
1(0.1%)
4(0.2%)
487(27.8%)
475(27.1%)
15(0.9%)
276(15.8%)
171(9.8%)
77(4.4%)
5(0.3%)
29(1.7%)
10(0.6%)
15(0.9%)
7(0.4%)
36(2.1%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor7 [factor] 1. 033 2. 034 3. 043 4. 044 5. 045 6. 053 7. 054 8. 055 9. 099 10. 23 11. 32 12. 33 13. 34 14. 35 15. 43 16. 44 17. 45 18. 53 19. 54 20. 55 21. 99 22. 998 23. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(37.5%)
5(31.2%)
0(0.0%)
3(18.8%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 8


All data

st_css() #IMPORTANT!
  cssitespecificfactor8 <- as.factor(trimws(d[,"cssitespecificfactor8"]))
  new.d <- data.frame(new.d, cssitespecificfactor8)
  new.d <- apply_labels(new.d, cssitespecificfactor8 = "cs_site_specific_factor8")
temp.d <- data.frame (new.d.1, cssitespecificfactor8)
summarytools::view(dfSummary(new.d$cssitespecificfactor8, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor8 [labelled, factor] cs_site_specific_factor8 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
220(6.3%)
280(8.0%)
51(1.5%)
24(0.7%)
1(0.0%)
23(0.7%)
1(0.0%)
6(0.2%)
859(24.6%)
1379(39.5%)
367(10.5%)
215(6.2%)
12(0.3%)
54(1.5%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
1(0.3%)
0(0.0%)
114(35.5%)
140(43.6%)
28(8.7%)
31(9.7%)
1(0.3%)
4(1.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.0%)
0(0.0%)
0(0.0%)
76(36.2%)
105(50.0%)
13(6.2%)
11(5.2%)
1(0.5%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.3%)
0(0.0%)
1(0.3%)
100(31.7%)
136(43.2%)
41(13.0%)
25(7.9%)
2(0.6%)
6(1.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
40(13.6%)
98(33.2%)
7(2.4%)
7(2.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
23(7.8%)
79(26.8%)
29(9.8%)
10(3.4%)
1(0.3%)
1(0.3%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
123(21.1%)
121(20.7%)
27(4.6%)
8(1.4%)
0(0.0%)
4(0.7%)
0(0.0%)
0(0.0%)
46(7.9%)
152(26.0%)
63(10.8%)
32(5.5%)
0(0.0%)
8(1.4%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
57(3.3%)
61(3.5%)
17(1.0%)
9(0.5%)
1(0.1%)
11(0.6%)
0(0.0%)
5(0.3%)
493(28.2%)
759(43.3%)
192(11.0%)
106(6.1%)
7(0.4%)
33(1.9%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor8 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 4 8. 5 9. 6 10. 7 11. 8 12. 9 13. 998 14. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(43.8%)
8(50.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 9


All data

st_css() #IMPORTANT!
  cssitespecificfactor9 <- as.factor(trimws(d[,"cssitespecificfactor9"]))
  new.d <- data.frame(new.d, cssitespecificfactor9)
  new.d <- apply_labels(new.d, cssitespecificfactor9 = "cs_site_specific_factor9")
temp.d <- data.frame (new.d.1, cssitespecificfactor9)
summarytools::view(dfSummary(new.d$cssitespecificfactor9, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor9 [labelled, factor] cs_site_specific_factor9 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
26(0.7%)
92(2.6%)
3(0.1%)
32(0.9%)
12(0.3%)
7(0.2%)
2(0.1%)
1(0.0%)
2(0.1%)
178(5.1%)
528(15.1%)
11(0.3%)
244(7.0%)
49(1.4%)
65(1.9%)
2(0.1%)
15(0.4%)
5(0.1%)
2(0.1%)
2166(62.0%)
51(1.5%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
28(8.7%)
63(19.6%)
0(0.0%)
34(10.6%)
10(3.1%)
8(2.5%)
0(0.0%)
3(0.9%)
0(0.0%)
1(0.3%)
169(52.6%)
5(1.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(2.9%)
28(13.3%)
0(0.0%)
15(7.1%)
1(0.5%)
3(1.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
154(73.3%)
3(1.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
31(9.8%)
70(22.2%)
2(0.6%)
40(12.7%)
3(1.0%)
8(2.5%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
155(49.2%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
6(2.0%)
27(9.2%)
0(0.0%)
9(3.1%)
5(1.7%)
4(1.4%)
1(0.3%)
0(0.0%)
0(0.0%)
10(3.4%)
31(10.5%)
1(0.3%)
21(7.1%)
1(0.3%)
9(3.1%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
164(55.6%)
4(1.4%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
12(2.1%)
45(7.7%)
3(0.5%)
16(2.7%)
2(0.3%)
1(0.2%)
1(0.2%)
0(0.0%)
0(0.0%)
26(4.5%)
93(15.9%)
3(0.5%)
30(5.1%)
15(2.6%)
10(1.7%)
1(0.2%)
1(0.2%)
1(0.2%)
0(0.0%)
309(52.9%)
15(2.6%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
8(0.5%)
20(1.1%)
0(0.0%)
7(0.4%)
5(0.3%)
2(0.1%)
0(0.0%)
1(0.1%)
2(0.1%)
76(4.3%)
241(13.8%)
5(0.3%)
101(5.8%)
19(1.1%)
27(1.5%)
0(0.0%)
10(0.6%)
2(0.1%)
1(0.1%)
1205(68.8%)
20(1.1%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor9 [factor] 1. 033 2. 034 3. 035 4. 043 5. 044 6. 045 7. 054 8. 055 9. 32 10. 33 11. 34 12. 35 13. 43 14. 44 15. 45 16. 53 17. 54 18. 55 19. 99 20. 998 21. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
2(12.5%)
0(0.0%)
3(18.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
10(62.5%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 10


All data

st_css() #IMPORTANT!
  cssitespecificfactor10 <- as.factor(trimws(d[,"cssitespecificfactor10"]))
  new.d <- data.frame(new.d, cssitespecificfactor10)
  new.d <- apply_labels(new.d, cssitespecificfactor10 = "cs_site_specific_factor10")
temp.d <- data.frame (new.d.1, cssitespecificfactor10)
summarytools::view(dfSummary(new.d$cssitespecificfactor10, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor10 [labelled, factor] cs_site_specific_factor10 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
26(0.7%)
124(3.5%)
15(0.4%)
9(0.3%)
1(0.0%)
5(0.1%)
2(0.1%)
180(5.2%)
776(22.2%)
62(1.8%)
80(2.3%)
2168(62.1%)
45(1.3%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
28(8.7%)
97(30.2%)
10(3.1%)
11(3.4%)
169(52.6%)
6(1.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(2.9%)
43(20.5%)
1(0.5%)
3(1.4%)
154(73.3%)
3(1.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
31(9.8%)
110(34.9%)
6(1.9%)
8(2.5%)
155(49.2%)
4(1.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
6(2.0%)
36(12.2%)
5(1.7%)
5(1.7%)
0(0.0%)
1(0.3%)
0(0.0%)
10(3.4%)
52(17.6%)
2(0.7%)
10(3.4%)
164(55.6%)
4(1.4%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
12(2.1%)
61(10.4%)
5(0.9%)
2(0.3%)
0(0.0%)
1(0.2%)
0(0.0%)
26(4.5%)
123(21.1%)
19(3.3%)
11(1.9%)
309(52.9%)
15(2.6%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
8(0.5%)
27(1.5%)
5(0.3%)
2(0.1%)
1(0.1%)
2(0.1%)
2(0.1%)
78(4.5%)
346(19.7%)
24(1.4%)
37(2.1%)
1207(68.9%)
13(0.7%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor10 [factor] 1. 006 2. 007 3. 008 4. 009 5. 010 6. 10 7. 5 8. 6 9. 7 10. 8 11. 9 12. 998 13. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
5(31.2%)
0(0.0%)
0(0.0%)
10(62.5%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 11


All data

st_css() #IMPORTANT!
  cssitespecificfactor11 <- as.factor(trimws(d[,"cssitespecificfactor11"]))
  new.d <- data.frame(new.d, cssitespecificfactor11)
  new.d <- apply_labels(new.d, cssitespecificfactor11 = "cs_site_specific_factor11")
temp.d <- data.frame (new.d.1, cssitespecificfactor11)
summarytools::view(dfSummary(new.d$cssitespecificfactor11, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor11 [labelled, factor] cs_site_specific_factor11 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
2(0.1%)
5(0.1%)
19(0.5%)
29(0.8%)
46(1.3%)
123(3.5%)
248(7.1%)
2090(59.8%)
931(26.7%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
4(1.2%)
2(0.6%)
15(4.7%)
20(6.2%)
163(50.8%)
117(36.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(2.9%)
11(5.2%)
6(2.9%)
152(72.4%)
35(16.7%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(2.2%)
20(6.3%)
18(5.7%)
163(51.7%)
107(34.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
2(0.7%)
0(0.0%)
6(2.0%)
3(1.0%)
1(0.3%)
8(2.7%)
91(30.8%)
117(39.7%)
67(22.7%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
0(0.0%)
3(0.5%)
8(1.4%)
7(1.2%)
8(1.4%)
27(4.6%)
16(2.7%)
309(52.9%)
206(35.3%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
0(0.0%)
2(0.1%)
5(0.3%)
15(0.9%)
22(1.3%)
42(2.4%)
94(5.4%)
1178(67.2%)
394(22.5%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor11 [factor] 1. 030 2. 040 3. 050 4. 30 5. 40 6. 50 7. 988 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
8(50.0%)
5(31.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 12


All data

st_css() #IMPORTANT!
  cssitespecificfactor12 <- as.factor(trimws(d[,"cssitespecificfactor12"]))
  new.d <- data.frame(new.d, cssitespecificfactor12)
  new.d <- apply_labels(new.d, cssitespecificfactor12 = "cs_site_specific_factor12")
temp.d <- data.frame (new.d.1, cssitespecificfactor12)
summarytools::view(dfSummary(new.d$cssitespecificfactor12, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor12 [labelled, factor] cs_site_specific_factor12 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
5(0.1%)
1(0.0%)
109(3.1%)
69(2.0%)
57(1.6%)
65(1.9%)
44(1.3%)
52(1.5%)
25(0.7%)
22(0.6%)
17(0.5%)
10(0.3%)
12(0.3%)
10(0.3%)
3(0.1%)
1(0.0%)
369(10.6%)
90(2.6%)
77(2.2%)
91(2.6%)
15(0.4%)
15(0.4%)
6(0.2%)
6(0.2%)
2(0.1%)
2(0.1%)
2(0.1%)
321(9.2%)
2(0.1%)
1(0.0%)
1(0.0%)
3(0.1%)
2(0.1%)
295(8.4%)
1(0.0%)
1(0.0%)
268(7.7%)
241(6.9%)
224(6.4%)
140(4.0%)
125(3.6%)
89(2.5%)
475(13.6%)
43(1.2%)
83(2.4%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
38(11.8%)
4(1.2%)
10(3.1%)
8(2.5%)
2(0.6%)
2(0.6%)
0(0.0%)
2(0.6%)
1(0.3%)
0(0.0%)
1(0.3%)
32(10.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
39(12.1%)
0(0.0%)
0(0.0%)
25(7.8%)
19(5.9%)
19(5.9%)
14(4.4%)
15(4.7%)
11(3.4%)
66(20.6%)
4(1.2%)
7(2.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
35(16.7%)
7(3.3%)
2(1.0%)
9(4.3%)
2(1.0%)
1(0.5%)
2(1.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
22(10.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
25(11.9%)
0(0.0%)
0(0.0%)
25(11.9%)
17(8.1%)
12(5.7%)
14(6.7%)
8(3.8%)
7(3.3%)
16(7.6%)
2(1.0%)
2(1.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
31(9.8%)
16(5.1%)
15(4.8%)
8(2.5%)
1(0.3%)
0(0.0%)
2(0.6%)
1(0.3%)
0(0.0%)
0(0.0%)
1(0.3%)
26(8.3%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
28(8.9%)
0(0.0%)
0(0.0%)
33(10.5%)
30(9.5%)
30(9.5%)
13(4.1%)
13(4.1%)
8(2.5%)
47(14.9%)
4(1.3%)
6(1.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
0(0.0%)
0(0.0%)
17(5.8%)
23(7.8%)
16(5.4%)
20(6.8%)
12(4.1%)
15(5.1%)
12(4.1%)
8(2.7%)
6(2.0%)
6(2.0%)
2(0.7%)
1(0.3%)
1(0.3%)
0(0.0%)
15(5.1%)
6(2.0%)
3(1.0%)
4(1.4%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
14(4.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
14(4.7%)
0(0.0%)
0(0.0%)
9(3.1%)
20(6.8%)
22(7.5%)
12(4.1%)
7(2.4%)
4(1.4%)
18(6.1%)
5(1.7%)
2(0.7%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
1(0.2%)
1(0.2%)
64(11.0%)
30(5.1%)
27(4.6%)
32(5.5%)
22(3.8%)
25(4.3%)
10(1.7%)
9(1.5%)
8(1.4%)
4(0.7%)
3(0.5%)
3(0.5%)
2(0.3%)
0(0.0%)
24(4.1%)
10(1.7%)
11(1.9%)
7(1.2%)
3(0.5%)
3(0.5%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
28(4.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
29(5.0%)
0(0.0%)
0(0.0%)
28(4.8%)
31(5.3%)
34(5.8%)
13(2.2%)
18(3.1%)
4(0.7%)
77(13.2%)
9(1.5%)
13(2.2%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
2(0.1%)
0(0.0%)
28(1.6%)
16(0.9%)
14(0.8%)
13(0.7%)
10(0.6%)
12(0.7%)
3(0.2%)
5(0.3%)
3(0.2%)
0(0.0%)
7(0.4%)
6(0.3%)
0(0.0%)
1(0.1%)
223(12.7%)
47(2.7%)
36(2.1%)
55(3.1%)
7(0.4%)
9(0.5%)
2(0.1%)
1(0.1%)
0(0.0%)
2(0.1%)
0(0.0%)
195(11.1%)
1(0.1%)
1(0.1%)
0(0.0%)
3(0.2%)
1(0.1%)
160(9.1%)
1(0.1%)
1(0.1%)
148(8.5%)
124(7.1%)
106(6.1%)
73(4.2%)
64(3.7%)
53(3.0%)
246(14.0%)
19(1.1%)
53(3.0%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor12 [factor] 1. 0 2. 000 3. 001 4. 002 5. 003 6. 004 7. 005 8. 006 9. 007 10. 008 11. 009 12. 010 13. 011 14. 012 15. 013 16. 017 17. 1 18. 10 19. 11 20. 12 21. 13 22. 14 23. 15 24. 16 25. 17 26. 18 27. 19 28. 2 29. 20 30. 21 31. 22 32. 23 33. 26 34. 3 35. 35 36. 38 37. 4 38. 5 39. 6 40. 7 41. 8 42. 9 43. 991 44. 998 45. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
1(6.2%)
0(0.0%)
2(12.5%)
5(31.2%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 13


All data

st_css() #IMPORTANT!
  cssitespecificfactor13 <- as.factor(trimws(d[,"cssitespecificfactor13"]))
  new.d <- data.frame(new.d, cssitespecificfactor13)
  new.d <- apply_labels(new.d, cssitespecificfactor13 = "cs_site_specific_factor13")
temp.d <- data.frame (new.d.1, cssitespecificfactor13)
summarytools::view(dfSummary(new.d$cssitespecificfactor13, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor13 [labelled, factor] cs_site_specific_factor13 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
4(0.1%)
2(0.1%)
4(0.1%)
5(0.1%)
1(0.0%)
11(0.3%)
2(0.1%)
3(0.1%)
2(0.1%)
5(0.1%)
9(0.3%)
254(7.3%)
21(0.6%)
51(1.5%)
14(0.4%)
11(0.3%)
4(0.1%)
6(0.2%)
2(0.1%)
4(0.1%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
1(0.0%)
4(0.1%)
39(1.1%)
25(0.7%)
1339(38.3%)
89(2.5%)
217(6.2%)
71(2.0%)
72(2.1%)
31(0.9%)
45(1.3%)
30(0.9%)
12(0.3%)
13(0.4%)
12(0.3%)
7(0.2%)
4(0.1%)
9(0.3%)
2(0.1%)
7(0.2%)
8(0.2%)
6(0.2%)
2(0.1%)
8(0.2%)
3(0.1%)
1016(29.1%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
4(1.2%)
1(0.3%)
116(36.1%)
6(1.9%)
22(6.9%)
7(2.2%)
8(2.5%)
3(0.9%)
6(1.9%)
4(1.2%)
3(0.9%)
1(0.3%)
3(0.9%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
1(0.3%)
0(0.0%)
131(40.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(1.4%)
2(1.0%)
77(36.7%)
6(2.9%)
43(20.5%)
7(3.3%)
13(6.2%)
9(4.3%)
7(3.3%)
2(1.0%)
0(0.0%)
4(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
1(0.5%)
34(16.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(1.9%)
4(1.3%)
144(45.7%)
7(2.2%)
27(8.6%)
14(4.4%)
10(3.2%)
3(1.0%)
7(2.2%)
3(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
87(27.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
2(0.7%)
0(0.0%)
0(0.0%)
1(0.3%)
4(1.4%)
3(1.0%)
84(28.5%)
11(3.7%)
9(3.1%)
4(1.4%)
3(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
2(0.7%)
5(1.7%)
70(23.7%)
11(3.7%)
8(2.7%)
2(0.7%)
4(1.4%)
0(0.0%)
2(0.7%)
1(0.3%)
0(0.0%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
64(21.7%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
2(0.3%)
1(0.2%)
2(0.3%)
4(0.7%)
1(0.2%)
7(1.2%)
2(0.3%)
2(0.3%)
1(0.2%)
0(0.0%)
5(0.9%)
99(17.0%)
7(1.2%)
36(6.2%)
6(1.0%)
2(0.3%)
2(0.3%)
3(0.5%)
1(0.2%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(0.9%)
2(0.3%)
104(17.8%)
2(0.3%)
47(8.0%)
4(0.7%)
3(0.5%)
2(0.3%)
2(0.3%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.3%)
0(0.0%)
224(38.4%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
2(0.1%)
1(0.1%)
2(0.1%)
0(0.0%)
0(0.0%)
2(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
1(0.1%)
1(0.1%)
71(4.1%)
3(0.2%)
6(0.3%)
4(0.2%)
6(0.3%)
2(0.1%)
3(0.2%)
1(0.1%)
2(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
0(0.0%)
1(0.1%)
3(0.2%)
19(1.1%)
11(0.6%)
823(47.0%)
57(3.3%)
70(4.0%)
37(2.1%)
34(1.9%)
14(0.8%)
21(1.2%)
19(1.1%)
9(0.5%)
8(0.5%)
8(0.5%)
4(0.2%)
3(0.2%)
7(0.4%)
1(0.1%)
7(0.4%)
8(0.5%)
3(0.2%)
1(0.1%)
4(0.2%)
2(0.1%)
465(26.6%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor13 [factor] 1. 001 2. 002 3. 003 4. 004 5. 005 6. 006 7. 007 8. 008 9. 009 10. 010 11. 011 12. 012 13. 013 14. 014 15. 015 16. 016 17. 017 18. 018 19. 019 20. 020 21. 021 22. 022 23. 027 24. 029 25. 031 26. 052 27. 1 28. 10 29. 11 30. 12 31. 13 32. 14 33. 15 34. 16 35. 17 36. 18 37. 19 38. 2 39. 20 40. 21 41. 22 42. 23 43. 24 44. 25 45. 26 46. 27 47. 28 48. 29 49. 3 50. 30 [ 25 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
11(68.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 14


All data

st_css() #IMPORTANT!
  cssitespecificfactor14 <- as.factor(trimws(d[,"cssitespecificfactor14"]))
  new.d <- data.frame(new.d, cssitespecificfactor14)
  new.d <- apply_labels(new.d, cssitespecificfactor14 = "cs_site_specific_factor14")
temp.d <- data.frame (new.d.1, cssitespecificfactor14)
summarytools::view(dfSummary(new.d$cssitespecificfactor14, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor14 [labelled, factor] cs_site_specific_factor14 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
2(0.1%)
174(5.0%)
175(5.0%)
30(0.9%)
698(20.0%)
921(26.4%)
137(3.9%)
2(0.1%)
1180(33.8%)
38(1.1%)
135(3.9%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
113(35.2%)
142(44.2%)
18(5.6%)
0(0.0%)
44(13.7%)
2(0.6%)
2(0.6%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
93(44.3%)
93(44.3%)
1(0.5%)
0(0.0%)
18(8.6%)
3(1.4%)
1(0.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
114(36.2%)
137(43.5%)
25(7.9%)
2(0.6%)
27(8.6%)
2(0.6%)
7(2.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
0(0.0%)
22(7.5%)
20(6.8%)
1(0.3%)
7(2.4%)
18(6.1%)
7(2.4%)
0(0.0%)
217(73.6%)
2(0.7%)
1(0.3%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
0(0.0%)
105(18.0%)
98(16.8%)
14(2.4%)
84(14.4%)
130(22.3%)
19(3.3%)
0(0.0%)
95(16.3%)
10(1.7%)
29(5.0%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
0(0.0%)
47(2.7%)
57(3.3%)
15(0.9%)
284(16.2%)
399(22.8%)
67(3.8%)
0(0.0%)
768(43.9%)
19(1.1%)
95(5.4%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor14 [factor] 1. 0 2. 010 3. 020 4. 030 5. 10 6. 20 7. 30 8. 50 9. 988 10. 998 11. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
2(12.5%)
0(0.0%)
0(0.0%)
11(68.8%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 15


All data

st_css() #IMPORTANT!
  cssitespecificfactor15 <- as.factor(trimws(d[,"cssitespecificfactor15"]))
  new.d <- data.frame(new.d, cssitespecificfactor15)
  new.d <- apply_labels(new.d, cssitespecificfactor15 = "cs_site_specific_factor15")
temp.d <- data.frame (new.d.1, cssitespecificfactor15)
summarytools::view(dfSummary(new.d$cssitespecificfactor15, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor15 [labelled, factor] cs_site_specific_factor15 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
45(1.3%)
10(0.3%)
48(1.4%)
52(1.5%)
193(5.5%)
220(6.3%)
251(7.2%)
959(27.5%)
1187(34.0%)
527(15.1%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
8(2.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
20(6.2%)
30(9.3%)
193(60.1%)
40(12.5%)
30(9.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
4(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
42(20.0%)
20(9.5%)
116(55.2%)
18(8.6%)
10(4.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
7(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
35(11.1%)
36(11.4%)
184(58.4%)
28(8.9%)
25(7.9%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
0(0.0%)
0(0.0%)
5(1.7%)
3(1.0%)
23(7.8%)
4(1.4%)
2(0.7%)
13(4.4%)
228(77.3%)
17(5.8%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
9(1.5%)
6(1.0%)
27(4.6%)
28(4.8%)
123(21.1%)
26(4.5%)
28(4.8%)
148(25.3%)
100(17.1%)
89(15.2%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
17(1.0%)
4(0.2%)
16(0.9%)
21(1.2%)
47(2.7%)
93(5.3%)
135(7.7%)
302(17.2%)
761(43.5%)
355(20.3%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor15 [factor] 1. 0 2. 000 3. 010 4. 020 5. 030 6. 10 7. 20 8. 30 9. 988 10. 999
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(18.8%)
12(75.0%)
1(6.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 1


All data

st_css() #IMPORTANT!
  cssitespecificfactor1 <- as.numeric(trimws(d[,"cssitespecificfactor1"]))
  new.d <- data.frame(new.d, cssitespecificfactor1)
  new.d <- apply_labels(new.d, cssitespecificfactor1 = "cs_site_specific_factor1")
temp.d <- data.frame (new.d.1, cssitespecificfactor1)
summarytools::view(dfSummary(new.d$cssitespecificfactor1, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor1 [labelled, numeric] cs_site_specific_factor1 Mean (sd) : 213.9 (309.5) min < med < max: 1 < 75 < 999 IQR (CV) : 103.2 (1.4) 411 distinct values 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 189.7 (276.4) min < med < max: 1 < 79 < 999 IQR (CV) : 93 (1.5) 143 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 168.1 (238.6) min < med < max: 4 < 81 < 999 IQR (CV) : 76 (1.4) 119 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 215.2 (309.8) min < med < max: 7 < 79 < 999 IQR (CV) : 90.5 (1.4) 137 distinct values 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 177.1 (268.2) min < med < max: 5 < 67 < 999 IQR (CV) : 98.5 (1.5) 137 distinct values 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 171.2 (255.4) min < med < max: 6 < 70 < 999 IQR (CV) : 88.2 (1.5) 196 distinct values 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 243.6 (340.6) min < med < max: 1 < 77 < 999 IQR (CV) : 126 (1.4) 292 distinct values 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor1 [numeric] Mean (sd) : 258.2 (369.8) min < med < max: 38 < 86 < 999 IQR (CV) : 105.5 (1.4)
38:1(6.2%)
39:1(6.2%)
47:1(6.2%)
52:1(6.2%)
56:1(6.2%)
61:1(6.2%)
71:1(6.2%)
82:1(6.2%)
90:1(6.2%)
114:1(6.2%)
141:1(6.2%)
148:1(6.2%)
198:1(6.2%)
997:1(6.2%)
999:2(12.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 2


All data

st_css() #IMPORTANT!
  cssitespecificfactor2 <- as.factor(trimws(d[,"cssitespecificfactor2"]))
  new.d <- data.frame(new.d, cssitespecificfactor2)
  new.d <- apply_labels(new.d, cssitespecificfactor2 = "cs_site_specific_factor2")
temp.d <- data.frame (new.d.1, cssitespecificfactor2)
summarytools::view(dfSummary(new.d$cssitespecificfactor2, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor2 [labelled, factor] cs_site_specific_factor2 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
535(15.3%)
24(0.7%)
3(0.1%)
2674(76.6%)
79(2.3%)
7(0.2%)
18(0.5%)
10(0.3%)
142(4.1%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
299(93.1%)
12(3.7%)
1(0.3%)
0(0.0%)
1(0.3%)
8(2.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
194(92.4%)
8(3.8%)
1(0.5%)
1(0.5%)
1(0.5%)
5(2.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
285(90.5%)
13(4.1%)
0(0.0%)
1(0.3%)
2(0.6%)
14(4.4%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
146(49.5%)
4(1.4%)
0(0.0%)
130(44.1%)
4(1.4%)
0(0.0%)
5(1.7%)
0(0.0%)
6(2.0%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
255(43.7%)
17(2.9%)
3(0.5%)
286(49.0%)
7(1.2%)
0(0.0%)
1(0.2%)
2(0.3%)
13(2.2%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
134(7.7%)
3(0.2%)
0(0.0%)
1467(83.8%)
35(2.0%)
5(0.3%)
9(0.5%)
4(0.2%)
94(5.4%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor2 [factor] 1. 010 2. 020 3. 030 4. 10 5. 20 6. 30 7. 997 8. 998 9. 999
0(0.0%)
0(0.0%)
0(0.0%)
13(81.2%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
2(12.5%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 3


All data

st_css() #IMPORTANT!
  cssitespecificfactor3 <- as.factor(trimws(d[,"cssitespecificfactor3"]))
  new.d <- data.frame(new.d, cssitespecificfactor3)
  new.d <- apply_labels(new.d, cssitespecificfactor3 = "cs_site_specific_factor3")
temp.d <- data.frame (new.d.1, cssitespecificfactor3)
summarytools::view(dfSummary(new.d$cssitespecificfactor3, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor3 [labelled, factor] cs_site_specific_factor3 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
1(0.0%)
9(0.3%)
59(1.7%)
14(0.4%)
680(19.5%)
58(1.7%)
2(0.1%)
2(0.1%)
1(0.0%)
17(0.5%)
8(0.2%)
2(0.1%)
1(0.0%)
65(1.9%)
60(1.7%)
78(2.2%)
11(0.3%)
42(1.2%)
27(0.8%)
17(0.5%)
123(3.5%)
12(0.3%)
2(0.1%)
3(0.1%)
2(0.1%)
65(1.9%)
2112(60.5%)
11(0.3%)
9(0.3%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
0(0.0%)
2(0.6%)
6(1.9%)
3(0.9%)
81(25.2%)
0(0.0%)
0(0.0%)
2(0.6%)
0(0.0%)
4(1.2%)
2(0.6%)
0(0.0%)
1(0.3%)
8(2.5%)
8(2.5%)
14(4.4%)
1(0.3%)
4(1.2%)
1(0.3%)
0(0.0%)
12(3.7%)
3(0.9%)
0(0.0%)
0(0.0%)
0(0.0%)
3(0.9%)
164(51.1%)
1(0.3%)
1(0.3%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
0(0.0%)
0(0.0%)
5(2.4%)
0(0.0%)
17(8.1%)
0(0.0%)
1(0.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
5(2.4%)
6(2.9%)
7(3.3%)
1(0.5%)
3(1.4%)
3(1.4%)
0(0.0%)
4(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.5%)
2(1.0%)
154(73.3%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
0(0.0%)
3(1.0%)
8(2.5%)
1(0.3%)
82(26.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
4(1.3%)
0(0.0%)
1(0.3%)
0(0.0%)
12(3.8%)
13(4.1%)
8(2.5%)
2(0.6%)
7(2.2%)
3(1.0%)
0(0.0%)
11(3.5%)
3(1.0%)
1(0.3%)
0(0.0%)
0(0.0%)
4(1.3%)
151(47.9%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
0(0.0%)
0(0.0%)
4(1.4%)
2(0.7%)
63(21.4%)
3(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.7%)
2(0.7%)
0(0.0%)
0(0.0%)
10(3.4%)
3(1.0%)
13(4.4%)
1(0.3%)
6(2.0%)
0(0.0%)
1(0.3%)
19(6.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(0.7%)
161(54.6%)
3(1.0%)
0(0.0%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
0(0.0%)
1(0.2%)
13(2.2%)
2(0.3%)
135(23.1%)
5(0.9%)
0(0.0%)
0(0.0%)
1(0.2%)
3(0.5%)
2(0.3%)
0(0.0%)
0(0.0%)
17(2.9%)
15(2.6%)
15(2.6%)
5(0.9%)
11(1.9%)
5(0.9%)
3(0.5%)
26(4.5%)
2(0.3%)
1(0.2%)
1(0.2%)
0(0.0%)
17(2.9%)
299(51.2%)
3(0.5%)
2(0.3%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
1(0.1%)
3(0.2%)
23(1.3%)
6(0.3%)
298(17.0%)
49(2.8%)
1(0.1%)
0(0.0%)
0(0.0%)
3(0.2%)
2(0.1%)
0(0.0%)
0(0.0%)
13(0.7%)
15(0.9%)
21(1.2%)
1(0.1%)
11(0.6%)
14(0.8%)
13(0.7%)
51(2.9%)
4(0.2%)
0(0.0%)
2(0.1%)
1(0.1%)
37(2.1%)
1173(67.0%)
4(0.2%)
6(0.3%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor3 [factor] 1. 0 2. 200 3. 210 4. 220 5. 230 6. 300 7. 320 8. 330 9. 340 10. 350 11. 400 12. 402 13. 404 14. 406 15. 415 16. 420 17. 430 18. 480 19. 482 20. 483 21. 485 22. 490 23. 495 24. 500 25. 950 26. 960 27. 970 28. 980 29. 990
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
10(62.5%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 4


All data

st_css() #IMPORTANT!
  cssitespecificfactor4 <- as.factor(trimws(d[,"cssitespecificfactor4"]))
  new.d <- data.frame(new.d, cssitespecificfactor4)
  new.d <- apply_labels(new.d, cssitespecificfactor4 = "cs_site_specific_factor4")
temp.d <- data.frame (new.d.1, cssitespecificfactor4)
summarytools::view(dfSummary(new.d$cssitespecificfactor4, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor4 [labelled, factor] cs_site_specific_factor4 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
26(0.7%)
1(0.0%)
3(0.1%)
52(1.5%)
11(0.3%)
12(0.3%)
1(0.0%)
1(0.0%)
119(3.4%)
2(0.1%)
1(0.0%)
7(0.2%)
1(0.0%)
3(0.1%)
22(0.6%)
2(0.1%)
5(0.1%)
1(0.0%)
451(12.9%)
2771(79.4%)
 65 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
2(0.6%)
0(0.0%)
0(0.0%)
6(1.9%)
3(0.9%)
2(0.6%)
0(0.0%)
0(0.0%)
14(4.4%)
2(0.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
23(7.2%)
267(83.2%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
0(0.0%)
0(0.0%)
1(0.5%)
10(4.8%)
2(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
10(4.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
9(4.3%)
178(84.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
0(0.0%)
0(0.0%)
1(0.3%)
2(0.6%)
3(1.0%)
2(0.6%)
0(0.0%)
1(0.3%)
6(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
24(7.6%)
274(87.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
13(4.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
5(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
26(8.8%)
249(84.4%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
3(0.5%)
0(0.0%)
1(0.2%)
5(0.9%)
3(0.5%)
3(0.5%)
1(0.2%)
0(0.0%)
39(6.7%)
0(0.0%)
0(0.0%)
1(0.2%)
0(0.0%)
0(0.0%)
1(0.2%)
2(0.3%)
1(0.2%)
0(0.0%)
61(10.4%)
463(79.3%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
8(0.5%)
1(0.1%)
0(0.0%)
29(1.7%)
0(0.0%)
4(0.2%)
0(0.0%)
0(0.0%)
45(2.6%)
0(0.0%)
1(0.1%)
6(0.3%)
1(0.1%)
3(0.2%)
19(1.1%)
0(0.0%)
1(0.1%)
1(0.1%)
307(17.5%)
1325(75.7%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor4 [factor] 1. 110 2. 120 3. 140 4. 150 5. 210 6. 220 7. 230 8. 240 9. 250 10. 310 11. 330 12. 350 13. 430 14. 440 15. 450 16. 510 17. 520 18. 540 19. 550 20. 988
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
15(93.8%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 5


All data

st_css() #IMPORTANT!
  cssitespecificfactor5 <- as.factor(trimws(d[,"cssitespecificfactor5"]))
  new.d <- data.frame(new.d, cssitespecificfactor5)
  new.d <- apply_labels(new.d, cssitespecificfactor5 = "cs_site_specific_factor5")
temp.d <- data.frame (new.d.1, cssitespecificfactor5)
summarytools::view(dfSummary(new.d$cssitespecificfactor5, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor5 [labelled, factor] cs_site_specific_factor5 1. 988
3493(100.0%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
295(100.0%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
584(100.0%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
1752(100.0%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor5 [factor] 1. 988
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


CS SITE-SPECIFIC FACTOR 6


All data

st_css() #IMPORTANT!
  cssitespecificfactor6 <- as.factor(trimws(d[,"cssitespecificfactor6"]))
  new.d <- data.frame(new.d, cssitespecificfactor6)
  new.d <- apply_labels(new.d, cssitespecificfactor6 = "cs_site_specific_factor6")
temp.d <- data.frame (new.d.1, cssitespecificfactor6)
summarytools::view(dfSummary(new.d$cssitespecificfactor6, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 cssitespecificfactor6 [labelled, factor] cs_site_specific_factor6 1. 988
3493(100.0%)
 64 (1.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
321(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
210(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
315(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
295(100.0%)
 61 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
584(100.0%)
 1 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
1752(100.0%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 cs_site_specific_factor6 [factor] 1. 988
16(100.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-6 T

  • Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for the AJCC 6th edition “T” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.

  • Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2940


All data

st_css() #IMPORTANT!
  derivedajcc6t <- as.factor(trimws(d[,"derivedajcc6t"]))
  new.d <- data.frame(new.d, derivedajcc6t)
  new.d <- apply_labels(new.d, derivedajcc6t = "derived_ajcc_6t")
temp.d <- data.frame (new.d.1, derivedajcc6t)
summarytools::view(dfSummary(new.d$derivedajcc6t, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc6t [labelled, factor] derived_ajcc_6t 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
16(0.7%)
8(0.3%)
1034(43.9%)
2(0.1%)
83(3.5%)
32(1.4%)
537(22.8%)
316(13.4%)
121(5.1%)
90(3.8%)
12(0.5%)
35(1.5%)
68(2.9%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
0(0.0%)
0(0.0%)
25(24.5%)
0(0.0%)
5(4.9%)
5(4.9%)
43(42.2%)
3(2.9%)
13(12.7%)
6(5.9%)
0(0.0%)
2(2.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
0(0.0%)
1(1.1%)
47(53.4%)
1(1.1%)
8(9.1%)
3(3.4%)
11(12.5%)
7(8.0%)
7(8.0%)
1(1.1%)
1(1.1%)
0(0.0%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
2(1.4%)
1(0.7%)
49(34.8%)
0(0.0%)
7(5.0%)
2(1.4%)
48(34.0%)
8(5.7%)
12(8.5%)
9(6.4%)
1(0.7%)
1(0.7%)
1(0.7%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
2(1.5%)
0(0.0%)
45(33.3%)
0(0.0%)
4(3.0%)
2(1.5%)
48(35.6%)
14(10.4%)
9(6.7%)
7(5.2%)
2(1.5%)
1(0.7%)
1(0.7%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
1(0.8%)
1(0.8%)
31(25.8%)
0(0.0%)
7(5.8%)
2(1.7%)
46(38.3%)
12(10.0%)
11(9.2%)
5(4.2%)
0(0.0%)
4(3.3%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
11(0.6%)
5(0.3%)
832(47.5%)
1(0.1%)
52(3.0%)
18(1.0%)
336(19.2%)
267(15.2%)
69(3.9%)
62(3.5%)
8(0.5%)
26(1.5%)
65(3.7%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6t [factor] 1. 12 2. 15 3. 18 4. 19 5. 21 6. 22 7. 23 8. 29 9. 31 10. 32 11. 39 12. 40 13. 99
0(0.0%)
0(0.0%)
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
5(31.2%)
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(6.2%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-6 N

  • Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for AJCC 6th edition “N” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.

  • Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2960


All data

st_css() #IMPORTANT!
  derivedajcc6n <- as.factor(trimws(d[,"derivedajcc6n"]))
  new.d <- data.frame(new.d, derivedajcc6n)
  new.d <- apply_labels(new.d, derivedajcc6n = "derived_ajcc_6n")
temp.d <- data.frame (new.d.1, derivedajcc6n)
summarytools::view(dfSummary(new.d$derivedajcc6n, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc6n [labelled, factor] derived_ajcc_6n 1. 0 2. 00 3. 10 4. 99
2004(85.1%)
217(9.2%)
77(3.3%)
56(2.4%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
94(92.2%)
0(0.0%)
8(7.8%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
85(96.6%)
0(0.0%)
2(2.3%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
127(90.1%)
0(0.0%)
11(7.8%)
3(2.1%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
57(42.2%)
71(52.6%)
5(3.7%)
2(1.5%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
111(92.5%)
3(2.5%)
5(4.2%)
1(0.8%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
1514(86.4%)
143(8.2%)
46(2.6%)
49(2.8%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6n [factor] 1. 0 2. 00 3. 10 4. 99
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-6 M

  • Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for AJCC 6th edition “M” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.

  • Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2980


All data

st_css() #IMPORTANT!
  derivedajcc6m <- as.factor(trimws(d[,"derivedajcc6m"]))
  new.d <- data.frame(new.d, derivedajcc6m)
  new.d <- apply_labels(new.d, derivedajcc6m = "derived_ajcc_6m")
temp.d <- data.frame (new.d.1, derivedajcc6m)
summarytools::view(dfSummary(new.d$derivedajcc6m, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc6m [labelled, factor] derived_ajcc_6m 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
2045(86.9%)
221(9.4%)
10(0.4%)
34(1.4%)
3(0.1%)
1(0.0%)
40(1.7%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
100(98.0%)
0(0.0%)
1(1.0%)
1(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
86(97.7%)
0(0.0%)
1(1.1%)
1(1.1%)
0(0.0%)
0(0.0%)
0(0.0%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
136(96.5%)
0(0.0%)
0(0.0%)
3(2.1%)
0(0.0%)
0(0.0%)
2(1.4%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
56(41.5%)
73(54.1%)
1(0.7%)
5(3.7%)
0(0.0%)
0(0.0%)
0(0.0%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
115(95.8%)
3(2.5%)
0(0.0%)
2(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
1536(87.7%)
145(8.3%)
7(0.4%)
22(1.3%)
3(0.2%)
1(0.1%)
38(2.2%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6m [factor] 1. 0 2. 00 3. 11 4. 12 5. 13 6. 19 7. 99
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-6 STAGE GRP

  • Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for the AJCC 6th edition “Stage Group” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.

  • Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3000


All data

st_css() #IMPORTANT!
  derivedajcc6stagegrp <- as.factor(trimws(d[,"derivedajcc6stagegrp"]))
  new.d <- data.frame(new.d, derivedajcc6stagegrp)
  new.d <- apply_labels(new.d, derivedajcc6stagegrp = "derived_ajcc_6_stage_grp")
temp.d <- data.frame (new.d.1, derivedajcc6stagegrp)
summarytools::view(dfSummary(new.d$derivedajcc6stagegrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc6stagegrp [labelled, factor] derived_ajcc_6_stage_grp 1. 10 2. 30 3. 50 4. 70 5. 99
13(0.6%)
1932(82.1%)
182(7.7%)
134(5.7%)
93(4.0%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
0(0.0%)
78(76.5%)
16(15.7%)
8(7.8%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
0(0.0%)
76(86.4%)
8(9.1%)
3(3.4%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
2(1.4%)
108(76.6%)
16(11.3%)
13(9.2%)
2(1.4%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
0(0.0%)
109(80.7%)
14(10.4%)
10(7.4%)
2(1.5%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
0(0.0%)
95(79.2%)
13(10.8%)
11(9.2%)
1(0.8%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
11(0.6%)
1451(82.8%)
115(6.6%)
88(5.0%)
87(5.0%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_6_stage_grp [factor] 1. 10 2. 30 3. 50 4. 70 5. 99
0(0.0%)
15(93.8%)
0(0.0%)
1(6.2%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SS1977

  • Description: This item is the derived “SEER Summary Stage 1977” from the CS algorithm (or EOD codes) effective with 2004 diagnosis.

  • Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3010


All data

st_css() #IMPORTANT!
  derivedss1977 <- as.factor(trimws(d[,"derivedss1977"]))
  new.d <- data.frame(new.d, derivedss1977)
  new.d <- apply_labels(new.d, derivedss1977 = "derived_ss1977")
temp.d <- data.frame (new.d.1, derivedss1977)
summarytools::view(dfSummary(new.d$derivedss1977, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedss1977 [labelled, factor] derived_ss1977 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
1971(83.7%)
206(8.8%)
25(1.1%)
36(1.5%)
49(2.1%)
67(2.8%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
78(76.5%)
16(15.7%)
2(2.0%)
4(3.9%)
2(2.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
76(86.4%)
8(9.1%)
1(1.1%)
1(1.1%)
1(1.1%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
111(78.7%)
16(11.3%)
3(2.1%)
7(5.0%)
3(2.1%)
1(0.7%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
111(82.2%)
14(10.4%)
1(0.7%)
3(2.2%)
6(4.4%)
0(0.0%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
96(80.0%)
16(13.3%)
2(1.7%)
3(2.5%)
3(2.5%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
1484(84.7%)
135(7.7%)
16(0.9%)
18(1.0%)
34(1.9%)
65(3.7%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss1977 [factor] 1. 1 2. 2 3. 3 4. 4 5. 7 6. 9
15(93.8%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SS2000

  • Description: This item is the derived “SEER Summary Stage 2000” from the CS algorithm (or EOD codes) effective with 2004 diagnosis.

  • Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.

  • Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3020


All data

st_css() #IMPORTANT!
  derivedss2000 <- as.factor(d[,"derivedss2000"])
  levels(derivedss2000) <- list(In_situ.0="0",
                                          Localized.1="1",
                                          Direct_extension.2="2",
                                          Lymph_nodes_only.3="3",
                                          Extension_nodes.4="4",
                                          Distant.7="7",
                                          Unknown.9="9")
    derivedss2000 <- relevel(derivedss2000, ref="Localized.1")
    new.d <- data.frame(new.d, derivedss2000)
    new.d <- apply_labels(new.d, derivedss2000 = "Tumor Staging")
    #summary(new.d$derivedss2000)
temp.d <- data.frame (new.d.1, derivedss2000)
summarytools::view(dfSummary(new.d$derivedss2000, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedss2000 [labelled, factor] Tumor Staging 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
1928(81.9%)
0(0.0%)
249(10.6%)
22(0.9%)
38(1.6%)
50(2.1%)
67(2.8%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
72(70.6%)
0(0.0%)
22(21.6%)
2(2.0%)
4(3.9%)
2(2.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
73(83.0%)
0(0.0%)
11(12.5%)
0(0.0%)
1(1.1%)
2(2.3%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
105(74.5%)
0(0.0%)
22(15.6%)
3(2.1%)
7(5.0%)
3(2.1%)
1(0.7%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
104(77.0%)
0(0.0%)
21(15.6%)
1(0.7%)
3(2.2%)
6(4.4%)
0(0.0%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
89(74.2%)
0(0.0%)
23(19.2%)
1(0.8%)
4(3.3%)
3(2.5%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
1470(83.9%)
0(0.0%)
149(8.5%)
15(0.9%)
19(1.1%)
34(1.9%)
65(3.7%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ss_2000 [factor] 1. Localized.1 2. In_situ.0 3. Direct_extension.2 4. Lymph_nodes_only.3 5. Extension_nodes.4 6. Distant.7 7. Unknown.9
15(93.8%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 1

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

    • 00000 No secondary diagnoses documented

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3110


All data

st_css() #IMPORTANT!
  comorbidcomplication1 <- as.factor(d[,"comorbidcomplication1"])
  levels(comorbidcomplication1) <- list(No_secondary.0="0")
    new.d <- data.frame(new.d, comorbidcomplication1)
    new.d <- apply_labels(new.d, comorbidcomplication1 = "comorbid_complication1")
    #summary(new.d$comorbidcomplication1)
temp.d <- data.frame (new.d.1, comorbidcomplication1)
summarytools::view(dfSummary(new.d$comorbidcomplication1, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication1 [labelled, factor] comorbid_complication1 1. No_secondary.0 All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication1 [factor] 1. No_secondary.0 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 2

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3120


All data

st_css() #IMPORTANT!
  comorbidcomplication2 <- as.factor(d[,"comorbidcomplication2"])
    new.d <- data.frame(new.d, comorbidcomplication2)
    new.d <- apply_labels(new.d, comorbidcomplication2 = "comorbid_complication2")
    #summary(new.d$comorbidcomplication2)
temp.d <- data.frame (new.d.1, comorbidcomplication2)
summarytools::view(dfSummary(new.d$comorbidcomplication2, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication2 [labelled, factor] comorbid_complication2 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 [ 20 others ]
1(0.7%)
7(5.0%)
1(0.7%)
1(0.7%)
2(1.4%)
2(1.4%)
5(3.6%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
2(1.4%)
1(0.7%)
3(2.1%)
1(0.7%)
1(0.7%)
1(0.7%)
8(5.7%)
1(0.7%)
1(0.7%)
1(0.7%)
2(1.4%)
1(0.7%)
1(0.7%)
1(0.7%)
29(20.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
4(2.9%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
1(0.7%)
2(1.4%)
1(0.7%)
1(0.7%)
35(25.0%)
 3417 (96.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 51. 60001 52. 60290 53. 605 
·
54. 60784 55. 62560 56. 64800 57. 69010 58. 71690 59. 7213 
·
60. 72660 61. 72950 62. 72981 63. 74685 64. 78020 65. 78060 66. 78843 67. 78863 68. 78900 69. 79093 70. 99990		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 51. 60001 52. 60290 53. 605 
·
54. 60784 55. 62560 56. 64800 57. 69010 58. 71690 59. 7213 
·
60. 72660 61. 72950 62. 72981 63. 74685 64. 78020 65. 78060 66. 78843 67. 78863 68. 78900 69. 79093 70. 99990		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 51. 60001 52. 60290 53. 605 
·
54. 60784 55. 62560 56. 64800 57. 69010 58. 71690 59. 7213 
·
60. 72660 61. 72950 62. 72981 63. 74685 64. 78020 65. 78060 66. 78843 67. 78863 68. 78900 69. 79093 70. 99990		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 [ 20 others ]
0(0.0%)
3(6.7%)
1(2.2%)
1(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
1(2.2%)
3(6.7%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
8(17.8%)
1(2.2%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
1(2.2%)
1(2.2%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
2(4.4%)
1(2.2%)
0(0.0%)
13(28.9%)
 311 (87.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 [ 20 others ]
1(1.1%)
4(4.4%)
0(0.0%)
0(0.0%)
2(2.2%)
2(2.2%)
5(5.6%)
1(1.1%)
1(1.1%)
1(1.1%)
0(0.0%)
2(2.2%)
1(1.1%)
2(2.2%)
1(1.1%)
1(1.1%)
0(0.0%)
5(5.6%)
1(1.1%)
0(0.0%)
1(1.1%)
2(2.2%)
1(1.1%)
1(1.1%)
0(0.0%)
19(21.1%)
0(0.0%)
0(0.0%)
1(1.1%)
0(0.0%)
1(1.1%)
1(1.1%)
0(0.0%)
3(3.3%)
1(1.1%)
0(0.0%)
1(1.1%)
1(1.1%)
1(1.1%)
0(0.0%)
1(1.1%)
0(0.0%)
0(0.0%)
1(1.1%)
0(0.0%)
0(0.0%)
1(1.1%)
0(0.0%)
0(0.0%)
1(1.1%)
22(24.4%)
 495 (84.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 51. 60001 52. 60290 53. 605 
·
54. 60784 55. 62560 56. 64800 57. 69010 58. 71690 59. 7213 
·
60. 72660 61. 72950 62. 72981 63. 74685 64. 78020 65. 78060 66. 78843 67. 78863 68. 78900 69. 79093 70. 99990		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication2 [factor] 1. 13644 2. 25000 3. 25080 4. 26890 5. 27200 6. 27210 7. 27240 8. 27280 9. 27400 10. 27490 11. 27620 12. 27800 13. 27801 14. 28590 15. 28750 16. 30502 17. 3051 
·
18. 30510 19. 33200 20. 33902 21. 35180 22. 36590 23. 40100 24. 40110 25. 4019 
·
26. 40190 27. 40391 28. 40493 29. 42400 30. 44390 31. 44422 32. 47790 33. 49390 34. 53081 35. 53550 36. 55090 37. 56010 38. 56410 39. 56800 40. 56930 41. 57150 42. 57390 43. 5780 
·
44. 57810 45. 5849 
·
46. 59410 47. 59500 48. 5989 
·
49. 59900 50. 60000 [ 20 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(40.0%)
0(0.0%)
1(20.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(20.0%)
0(0.0%)
1(20.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 11 (68.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 3

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3130


All data

st_css() #IMPORTANT!
  comorbidcomplication3 <- as.factor(d[,"comorbidcomplication3"])
    new.d <- data.frame(new.d, comorbidcomplication3)
    new.d <- apply_labels(new.d, comorbidcomplication3 = "comorbid_complication3")
    #summary(new.d$comorbidcomplication3)
temp.d <- data.frame (new.d.1, comorbidcomplication3)
summarytools::view(dfSummary(new.d$comorbidcomplication3, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication3 [labelled, factor] comorbid_complication3 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 [ 16 others ]
2(2.0%)
2(2.0%)
4(4.1%)
1(1.0%)
5(5.1%)
1(1.0%)
1(1.0%)
1(1.0%)
2(2.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
4(4.1%)
2(2.0%)
1(1.0%)
1(1.0%)
1(1.0%)
2(2.0%)
1(1.0%)
1(1.0%)
2(2.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
3(3.1%)
5(5.1%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
3(3.1%)
1(1.0%)
2(2.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
1(1.0%)
23(23.5%)
 3459 (97.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 51. 71690 52. 71696 53. 71946 54. 72240 55. 72291 56. 72950 57. 74685 58. 78057 59. 78821 60. 78841 61. 78843 62. 78863 63. 79029 64. 79093 65. 95919 66. 99990		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 51. 71690 52. 71696 53. 71946 54. 72240 55. 72291 56. 72950 57. 74685 58. 78057 59. 78821 60. 78841 61. 78843 62. 78863 63. 79029 64. 79093 65. 95919 66. 99990		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 51. 71690 52. 71696 53. 71946 54. 72240 55. 72291 56. 72950 57. 74685 58. 78057 59. 78821 60. 78841 61. 78843 62. 78863 63. 79029 64. 79093 65. 95919 66. 99990		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 [ 16 others ]
1(2.9%)
0(0.0%)
3(8.8%)
0(0.0%)
2(5.9%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
0(0.0%)
1(2.9%)
0(0.0%)
1(2.9%)
0(0.0%)
1(2.9%)
1(2.9%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
1(2.9%)
1(2.9%)
0(0.0%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
9(26.5%)
 322 (90.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 [ 16 others ]
1(1.7%)
2(3.3%)
1(1.7%)
1(1.7%)
3(5.0%)
1(1.7%)
0(0.0%)
0(0.0%)
2(3.3%)
0(0.0%)
1(1.7%)
1(1.7%)
1(1.7%)
3(5.0%)
1(1.7%)
1(1.7%)
1(1.7%)
1(1.7%)
2(3.3%)
1(1.7%)
0(0.0%)
0(0.0%)
1(1.7%)
1(1.7%)
1(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.7%)
0(0.0%)
1(1.7%)
0(0.0%)
2(3.3%)
4(6.7%)
1(1.7%)
1(1.7%)
1(1.7%)
0(0.0%)
0(0.0%)
2(3.3%)
0(0.0%)
1(1.7%)
1(1.7%)
1(1.7%)
0(0.0%)
1(1.7%)
1(1.7%)
1(1.7%)
0(0.0%)
14(23.3%)
 525 (89.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 51. 71690 52. 71696 53. 71946 54. 72240 55. 72291 56. 72950 57. 74685 58. 78057 59. 78821 60. 78841 61. 78843 62. 78863 63. 79029 64. 79093 65. 95919 66. 99990		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication3 [factor] 1. 07070 2. 24490 3. 25000 4. 27200 5. 27240 6. 27490 7. 2752 
·
8. 27651 9. 27800 10. 28590 11. 30002 12. 30491 13. 30500 14. 30510 15. 32723 16. 35400 17. 36589 18. 40110 19. 40190 20. 40390 21. 41040 22. 41200 23. 41400 24. 41490 25. 4186 
·
26. 42640 27. 42800 28. 42832 29. 43310 30. 43600 31. 44090 32. 44790 33. 49280 34. 49600 35. 53081 36. 56211 37. 57150 38. 57900 39. 58550 40. 58881 41. 60000 42. 60001 43. 60784 44. 68220 45. 69806 46. 700 
·
47. 7051 
·
48. 7070 
·
49. 71590 50. 71689 [ 16 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 12 (75.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 4

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3140


All data

st_css() #IMPORTANT!
  comorbidcomplication4 <- as.factor(d[,"comorbidcomplication4"])
    new.d <- data.frame(new.d, comorbidcomplication4)
    new.d <- apply_labels(new.d, comorbidcomplication4 = "comorbid_complication4")
    #summary(new.d$comorbidcomplication4)
temp.d <- data.frame (new.d.1, comorbidcomplication4)
summarytools::view(dfSummary(new.d$comorbidcomplication4, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication4 [labelled, factor] comorbid_complication4 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
3(4.6%)
2(3.1%)
1(1.5%)
1(1.5%)
2(3.1%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
3(4.6%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
4(6.2%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
2(3.1%)
1(1.5%)
2(3.1%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
2(3.1%)
1(1.5%)
1(1.5%)
3(4.6%)
1(1.5%)
2(3.1%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
2(3.1%)
 3492 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990
0(0.0%)
0(0.0%)
0(0.0%)
1(4.3%)
1(4.3%)
0(0.0%)
0(0.0%)
1(4.3%)
2(8.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(4.3%)
0(0.0%)
0(0.0%)
2(8.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(4.3%)
1(4.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(8.7%)
1(4.3%)
1(4.3%)
0(0.0%)
1(4.3%)
0(0.0%)
1(4.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(4.3%)
1(4.3%)
0(0.0%)
1(4.3%)
0(0.0%)
0(0.0%)
1(4.3%)
1(4.3%)
0(0.0%)
0(0.0%)
1(4.3%)
1(4.3%)
0(0.0%)
 333 (93.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990
1(2.6%)
1(2.6%)
1(2.6%)
0(0.0%)
1(2.6%)
2(5.3%)
1(2.6%)
0(0.0%)
0(0.0%)
1(2.6%)
1(2.6%)
1(2.6%)
0(0.0%)
1(2.6%)
1(2.6%)
1(2.6%)
1(2.6%)
1(2.6%)
1(2.6%)
0(0.0%)
3(7.9%)
1(2.6%)
0(0.0%)
1(2.6%)
1(2.6%)
1(2.6%)
0(0.0%)
0(0.0%)
1(2.6%)
1(2.6%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.6%)
2(5.3%)
1(2.6%)
0(0.0%)
2(5.3%)
1(2.6%)
1(2.6%)
1(2.6%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.6%)
1(2.6%)
0(0.0%)
0(0.0%)
2(5.3%)
 547 (93.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication4 [factor] 1. 07010 2. 11230 3. 25000 4. 2724 
·
5. 27240 6. 27490 7. 27610 8. 27620 9. 27800 10. 27802 11. 28249 12. 28590 13. 28860 14. 28981 15. 30503 16. 30510 17. 30981 18. 36501 19. 37862 20. 4019 
·
21. 40190 22. 41491 23. 4280 
·
24. 42890 25. 44390 26. 49390 27. 5180 
·
28. 53030 29. 53081 30. 56210 31. 58490 32. 58881 33. 5920 
·
34. 59200 35. 60000 36. 60001 37. 6011 
·
38. 60784 39. 71590 40. 72450 41. 73100 42. 78060 43. 78062 44. 78080 45. 78791 46. 78841 47. 78863 48. 79093 49. 99990
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 12 (75.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 5

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3150


All data

st_css() #IMPORTANT!
  comorbidcomplication5 <- as.factor(d[,"comorbidcomplication5"])
    new.d <- data.frame(new.d, comorbidcomplication5)
    new.d <- apply_labels(new.d, comorbidcomplication5 = "comorbid_complication5")
    #summary(new.d$comorbidcomplication5)
temp.d <- data.frame (new.d.1, comorbidcomplication5)
summarytools::view(dfSummary(new.d$comorbidcomplication5, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication5 [labelled, factor] comorbid_complication5 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990
1(2.3%)
3(6.8%)
2(4.5%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
2(4.5%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
6(13.6%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
2(4.5%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
 3513 (98.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990
0(0.0%)
1(7.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(7.1%)
1(7.1%)
2(14.3%)
0(0.0%)
1(7.1%)
0(0.0%)
0(0.0%)
2(14.3%)
1(7.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(7.1%)
0(0.0%)
0(0.0%)
1(7.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(7.1%)
0(0.0%)
1(7.1%)
0(0.0%)
0(0.0%)
1(7.1%)
0(0.0%)
 342 (96.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990
1(3.7%)
1(3.7%)
2(7.4%)
1(3.7%)
0(0.0%)
1(3.7%)
1(3.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.7%)
0(0.0%)
1(3.7%)
1(3.7%)
4(14.8%)
0(0.0%)
1(3.7%)
1(3.7%)
1(3.7%)
1(3.7%)
0(0.0%)
1(3.7%)
1(3.7%)
0(0.0%)
1(3.7%)
1(3.7%)
1(3.7%)
0(0.0%)
1(3.7%)
0(0.0%)
1(3.7%)
1(3.7%)
0(0.0%)
1(3.7%)
 558 (95.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication5 [factor] 1. 13500 2. 25000 3. 27240 4. 27803 5. 29600 6. 29630 7. 30501 8. 3051 
·
9. 30510 10. 30520 11. 35400 12. 36590 13. 36800 14. 37515 15. 40190 16. 42800 17. 43820 18. 45340 19. 53500 20. 59100 21. 5932 
·
22. 59971 23. 60784 24. 70000 25. 7032 
·
26. 78060 27. 78590 28. 78701 29. 78820 30. 78841 31. 78862 32. 79093 33. 79902 34. 99990
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 13 (81.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 6

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3160


All data

st_css() #IMPORTANT!
  comorbidcomplication6 <- as.factor(d[,"comorbidcomplication6"])
    new.d <- data.frame(new.d, comorbidcomplication6)
    new.d <- apply_labels(new.d, comorbidcomplication6 = "comorbid_complication6")
    #summary(new.d$comorbidcomplication6)
temp.d <- data.frame (new.d.1, comorbidcomplication6)
summarytools::view(dfSummary(new.d$comorbidcomplication6, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication6 [labelled, factor] comorbid_complication6 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093
1(3.1%)
2(6.2%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
2(6.2%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
1(3.1%)
2(6.2%)
1(3.1%)
2(6.2%)
1(3.1%)
2(6.2%)
1(3.1%)
1(3.1%)
1(3.1%)
2(6.2%)
 3525 (99.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093
1(14.3%)
0(0.0%)
1(14.3%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
1(14.3%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 349 (98.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093
0(0.0%)
2(9.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(4.5%)
1(4.5%)
1(4.5%)
2(9.1%)
1(4.5%)
1(4.5%)
1(4.5%)
0(0.0%)
0(0.0%)
1(4.5%)
1(4.5%)
1(4.5%)
0(0.0%)
0(0.0%)
2(9.1%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
2(9.1%)
 563 (96.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication6 [factor] 1. 2724 
·
2. 27240 3. 27800 4. 28521 5. 30401 6. 35180 7. 36211 8. 40190 9. 41400 10. 41490 11. 41519 12. 43812 13. 4720 
·
14. 5739 
·
15. 58490 16. 59390 17. 60010 18. 60784 19. 70583 20. 72420 21. 73390 22. 78060 23. 78659 24. 78865 25. 79029 26. 79093
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 13 (81.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 7

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3161


All data

st_css() #IMPORTANT!
  comorbidcomplication7 <- as.factor(d[,"comorbidcomplication7"])
    new.d <- data.frame(new.d, comorbidcomplication7)
    new.d <- apply_labels(new.d, comorbidcomplication7 = "comorbid_complication7")
    #summary(new.d$comorbidcomplication7)
temp.d <- data.frame (new.d.1, comorbidcomplication7)
summarytools::view(dfSummary(new.d$comorbidcomplication7, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication7 [labelled, factor] comorbid_complication7 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
1(4.3%)
1(4.3%)
1(4.3%)
2(8.7%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
1(4.3%)
 3534 (99.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 352 (98.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
1(6.2%)
0(0.0%)
1(6.2%)
1(6.2%)
0(0.0%)
1(6.2%)
1(6.2%)
0(0.0%)
1(6.2%)
0(0.0%)
0(0.0%)
1(6.2%)
1(6.2%)
0(0.0%)
 569 (97.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication7 [factor] 1. 25000 2. 26890 3. 27220 4. 27240 5. 27490 6. 27800 7. 28749 8. 30275 9. 31100 10. 32723 11. 36257 12. 41200 13. 41400 14. 42910 15. 58590 16. 72420 17. 78052 18. 78194 19. 78841 20. 79093 21. 81600 22. 9989 
·
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
 13 (81.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 8

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3162


All data

st_css() #IMPORTANT!
  comorbidcomplication8 <- as.factor(d[,"comorbidcomplication8"])
    new.d <- data.frame(new.d, comorbidcomplication8)
    new.d <- apply_labels(new.d, comorbidcomplication8 = "comorbid_complication8")
    #summary(new.d$comorbidcomplication8)
temp.d <- data.frame (new.d.1, comorbidcomplication8)
summarytools::view(dfSummary(new.d$comorbidcomplication8, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication8 [labelled, factor] comorbid_complication8 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
2(10.5%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
1(5.3%)
 3538 (99.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
1(25.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
 352 (98.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981
1(8.3%)
1(8.3%)
1(8.3%)
0(0.0%)
0(0.0%)
1(8.3%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
1(8.3%)
1(8.3%)
1(8.3%)
1(8.3%)
0(0.0%)
1(8.3%)
1(8.3%)
 573 (97.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication8 [factor] 1. 25000 2. 26890 3. 27240 4. 27390 5. 28521 6. 33829 7. 36511 8. 4019 
·
9. 41401 10. 53550 11. 56010 12. 56400 13. 71941 14. 75310 15. 78605 16. 78863 17. 79093 18. 79981
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(33.3%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 13 (81.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 9

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3163


All data

st_css() #IMPORTANT!
  comorbidcomplication9 <- as.factor(d[,"comorbidcomplication9"])
    new.d <- data.frame(new.d, comorbidcomplication9)
    new.d <- apply_labels(new.d, comorbidcomplication9 = "comorbid_complication9")
    #summary(new.d$comorbidcomplication9)
temp.d <- data.frame (new.d.1, comorbidcomplication9)
summarytools::view(dfSummary(new.d$comorbidcomplication9, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication9 [labelled, factor] comorbid_complication9 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
 3547 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 355 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843
1(16.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(16.7%)
0(0.0%)
1(16.7%)
1(16.7%)
1(16.7%)
1(16.7%)
 579 (99.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication9 [factor] 1. 24110 2. 32723 3. 41401 4. 49390 5. 60000 6. 71695 7. 72910 8. 73300 9. 78052 10. 78843
0(0.0%)
1(33.3%)
1(33.3%)
0(0.0%)
0(0.0%)
1(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 13 (81.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


COMORBID/COMPLICATION 10

  • Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.

  • Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.

  • Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.

  • Leave blank if no further secondary diagnosis.

  • Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3164


All data

st_css() #IMPORTANT!
  comorbidcomplication10 <- as.factor(d[,"comorbidcomplication10"])
    new.d <- data.frame(new.d, comorbidcomplication10)
    new.d <- apply_labels(new.d, comorbidcomplication10 = "comorbid_complication10")
    #summary(new.d$comorbidcomplication10)
temp.d <- data.frame (new.d.1, comorbidcomplication10)
summarytools::view(dfSummary(new.d$comorbidcomplication10, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 comorbidcomplication10 [labelled, factor] comorbid_complication10 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
 3550 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841		 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841		 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841		 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 355 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841
1(20.0%)
1(20.0%)
0(0.0%)
1(20.0%)
0(0.0%)
1(20.0%)
1(20.0%)
 580 (99.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841		 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 comorbid_complication10 [factor] 1. 25720 2. 26890 3. 32723 4. 33381 5. 4019 
·
6. 53560 7. 78841
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 15 (93.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


ICD REVISION COMORBID

  • Description: This item indicates the coding system in which the Comorbidities and Complications (secondary diagnoses) codes are provided.

  • Rationale: he CoC currently requires the collection and reporting of up to 10 ICD-9-CM codes describing secondary diagnoses for patients hospitalized for cancer treatment. Currently the use of ICD-10-CM is not mandatory in U.S. hospitals, though it may become so in the future. In the event this occurs cancer registries that maintain or collect this information will need to differentiate between ICD-9-CM and ICD-10-CM code use. The code values and definitions for this item would be expanded as necessary. Allowable codes reported in the Comorbidity and Complications items in STORE would be re-assessed at the same time.

  • Codes

    • 0 No comorbidities or complications recorded in patient’s record
    • 1 ICD-10-CM
    • 9 ICD-9-CM
    • Blank Comorbidities and Complications not collected

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3165


All data

st_css() #IMPORTANT!
  icdrevisioncomorbid <- as.factor(d[,"icdrevisioncomorbid"])
  levels(icdrevisioncomorbid) <- list(No_in_record.0="0",
                                        ICD_10_CM.1 = "1", 
                                         ICD_9_CM.9 = "9"
                                        )
    new.d <- data.frame(new.d, icdrevisioncomorbid)
    new.d <- apply_labels(new.d, icdrevisioncomorbid = "icd_revision_comorbid")
    #summary(new.d$icdrevisioncomorbid)
temp.d <- data.frame (new.d.1, icdrevisioncomorbid)
summarytools::view(dfSummary(new.d$icdrevisioncomorbid, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 icdrevisioncomorbid [labelled, factor] icd_revision_comorbid 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9
194(40.4%)
222(46.2%)
64(13.3%)
 3077 (86.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9
183(53.0%)
102(29.6%)
60(17.4%)
 11 (3.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9
0(0.0%)
120(100.0%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 icd_revision_comorbid [factor] 1. No_in_record.0 2. ICD_10_CM.1 3. ICD_9_CM.9
11(73.3%)
0(0.0%)
4(26.7%)
 1 (6.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE MST DEFN SRG

  • Description: Date of most definitive surgical resection of the primary site performed as part of the first course of treatment. See Chapter X for date format. Use RX DATE MST DEFN SRG FLAG [3171] if there is no appropriate or known date for this item.

Formerly RX Date–Most Defin Surg.

  • Rationale: This item is used to measure lag time between diagnosis and the most definitive surgery of the primary site or survival following the procedure. It also is used in conjunction with RX Date Surg Disch [3180] to calculate the duration of hospitalization following the most definitive primary site surgical procedure to evaluate treatment efficacy.

  • date var

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3170


All data

  rxdatemostdefinsurg <- trimws(d[,"rxdatemostdefinsurg"])
  #new.d.n <- data.frame(new.d.n, rxdatemostdefinsurg) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatemostdefinsurg), F, substr(rxdatemostdefinsurg, start=7, stop=8)=="99")
  rxdatemostdefinsurg[select99] <- substr(rxdatemostdefinsurg[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatemostdefinsurg), F, nchar(trimws(rxdatemostdefinsurg))==6)
  rxdatemostdefinsurg[select6] <- paste(rxdatemostdefinsurg[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatemostdefinsurg), F, nchar(trimws(rxdatemostdefinsurg))==4)
  rxdatemostdefinsurg[select4] <- paste(rxdatemostdefinsurg[select4], "0615", sep="")
  
  rxdatemostdefinsurg <- as.Date(rxdatemostdefinsurg, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatemostdefinsurg)
  new.d <- apply_labels(new.d, rxdatemostdefinsurg = "rx_date_most_defin_surg")
  temp.d <- data.frame (new.d.1, rxdatemostdefinsurg)
summarytools::view(dfSummary(new.d$rxdatemostdefinsurg, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatemostdefinsurg [labelled, Date] rx_date_most_defin_surg min : 2012-05-15 med : 2016-06-15 max : 2019-08-15 range : 7y 3m 0d 544 distinct values 2156 (60.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] 1. 2015-02-15 2. 2015-03-15 3. 2015-04-15 4. 2015-05-15 5. 2015-06-15 6. 2015-07-15 7. 2015-08-15 8. 2015-09-15 9. 2015-10-15 10. 2015-11-15 11. 2015-12-15 12. 2016-01-15 13. 2016-02-15 14. 2016-03-15 15. 2016-04-15 16. 2016-05-15 17. 2016-06-15 18. 2016-07-15 19. 2016-08-15 20. 2016-09-15 21. 2016-10-15 22. 2016-11-15 23. 2016-12-15 24. 2017-01-15 25. 2017-02-15 26. 2017-03-15 27. 2017-04-15 28. 2017-05-15 29. 2017-06-15 30. 2017-08-15 31. 2017-09-15 32. 2017-10-15 33. 2017-11-15 34. 2017-12-15 35. 2018-01-15 36. 2018-03-15
1(0.6%)
4(2.6%)
2(1.3%)
2(1.3%)
5(3.2%)
3(1.9%)
5(3.2%)
7(4.5%)
9(5.8%)
6(3.9%)
6(3.9%)
3(1.9%)
6(3.9%)
6(3.9%)
4(2.6%)
6(3.9%)
4(2.6%)
6(3.9%)
5(3.2%)
8(5.2%)
4(2.6%)
9(5.8%)
5(3.2%)
5(3.2%)
2(1.3%)
4(2.6%)
5(3.2%)
3(1.9%)
1(0.6%)
2(1.3%)
2(1.3%)
8(5.2%)
1(0.6%)
1(0.6%)
3(1.9%)
2(1.3%)
 166 (51.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] 1. 2015-04-15 2. 2015-05-15 3. 2015-07-15 4. 2015-09-15 5. 2015-10-15 6. 2015-11-15 7. 2015-12-15 8. 2016-01-15 9. 2016-02-15 10. 2016-03-15 11. 2016-05-15 12. 2016-06-15 13. 2016-07-15 14. 2016-08-15 15. 2016-09-15 16. 2016-10-15 17. 2016-11-15 18. 2016-12-15 19. 2017-01-15 20. 2017-02-15 21. 2017-03-15 22. 2017-04-15 23. 2017-09-15 24. 2017-10-15 25. 2018-01-15 26. 2018-05-15
1(1.6%)
2(3.3%)
2(3.3%)
2(3.3%)
3(4.9%)
2(3.3%)
1(1.6%)
3(4.9%)
4(6.6%)
2(3.3%)
2(3.3%)
2(3.3%)
3(4.9%)
5(8.2%)
4(6.6%)
1(1.6%)
2(3.3%)
4(6.6%)
3(4.9%)
2(3.3%)
2(3.3%)
2(3.3%)
3(4.9%)
1(1.6%)
2(3.3%)
1(1.6%)
 149 (71.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] 1. 2015-01-15 2. 2015-02-15 3. 2015-03-15 4. 2015-04-15 5. 2015-05-15 6. 2015-06-15 7. 2015-07-15 8. 2015-08-15 9. 2015-09-15 10. 2015-10-15 11. 2015-11-15 12. 2015-12-15 13. 2016-01-15 14. 2016-02-15 15. 2016-03-15 16. 2016-04-15 17. 2016-05-15 18. 2016-06-15 19. 2016-07-15 20. 2016-08-15 21. 2016-09-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-01-15 26. 2017-02-15 27. 2017-03-15 28. 2017-04-15 29. 2017-05-15 30. 2017-08-15 31. 2017-09-15 32. 2017-10-15 33. 2017-11-15 34. 2018-01-15
2(1.2%)
1(0.6%)
1(0.6%)
5(3.0%)
2(1.2%)
6(3.6%)
2(1.2%)
6(3.6%)
13(7.7%)
3(1.8%)
7(4.2%)
8(4.8%)
9(5.4%)
9(5.4%)
7(4.2%)
4(2.4%)
11(6.5%)
3(1.8%)
6(3.6%)
6(3.6%)
12(7.1%)
9(5.4%)
4(2.4%)
2(1.2%)
13(7.7%)
3(1.8%)
4(2.4%)
2(1.2%)
3(1.8%)
1(0.6%)
1(0.6%)
1(0.6%)
1(0.6%)
1(0.6%)
 147 (46.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] min : 2012-05-15 med : 2016-06-30 max : 2019-08-15 range : 7y 3m 0d 61 distinct values 196 (55.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] min : 2015-02-19 med : 2016-08-16 max : 2018-07-23 range : 3y 5m 4d 225 distinct values 310 (53.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] min : 2015-01-05 med : 2016-05-24 max : 2018-06-06 range : 3y 5m 1d 379 distinct values 1179 (67.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_most_defin_surg [Date] 1. 2012-06-15 2. 2012-09-15 3. 2013-03-15 4. 2013-11-15 5. 2014-02-15 6. 2014-05-15 7. 2014-11-15
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
 9 (56.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RAD–BOOST RX MODALITY

  • Description: Records the dominant modality of radiation therapy used to deliver the most clinically significant boost dose to the primary volume of interest during the first course of treatment. This is accomplished with external beam fields of reduced size (relative to the regional treatment fields), implants, stereotactic radiosurgery, conformal therapy, or intensity-modulated radiation therapy. External beam boosts may consist of two or more successive phases with progressively smaller fields, and they are generally coded as a single entity. This field is used with Rad–Regional RX Modality [1570].

  • Rationale: Radiation treatment frequently is delivered in two or more phases that can be summarized as regional and boost treatments. A boost dose is administered to a volume within the regional volume. For outcomes analysis, the modalities used for each of these phases can be very important

  • Codes

    • 00 No boost treatment
    • 20 External beam, NOS
    • 21 Orthovoltage
    • 22 Cobalt-60, Cesium-137
    • 23 Photons (2-5 MV)
    • 24 Photons (6-10 MV)
    • 25 Photons (11-19 MV)
    • 26 Photons (> 19 MV)
    • 27 Photons (mixed energies)
    • 28 Electrons
    • 29 Photons and electrons mixed
    • 30 Neutrons, with or without photons/electrons
    • 31 IMRT
    • 32 Conformal or 3-D therapy
    • 40 Protons
    • 41 Stereotactic radiosurgery, NOS
    • 42 Linac radiosurgery
    • 43 Gamma Knife
    • 50 Brachytherapy, NOS
    • 51 Brachytherapy, Intracavitary, LDR
    • 52 Brachytherapy, Intracavitary, HDR
    • 53 Brachytherapy, Interstitial, LDR
    • 54 Brachytherapy, Interstitial, HDR
    • 55 Radium
    • 60 Radio-isotopes, NOS
    • 61 Strontium - 89
    • 62 Strontium - 90
    • 98 Other, NOS
    • 99 Unknown

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3200


All data

st_css() #IMPORTANT!
  radboostrxmodality <- as.factor(d[,"radboostrxmodality"])
  levels(radboostrxmodality) <- list(No_boost_tx.0="0",
                                     External_beam_NOS.20="20",
                                     Photons_6_10_MV.24="24", 
                                     Photons_11_19_MV.25="25",
                                     Photons_mixed.27="27",
                                     Electrons.28 = "28",
                                     IMRT.31 = "31",
                                     Conformal_or_3D.32="32",
                                     Protons.40="40",
                                     Brachytherapy_NOS.50="50",
                                     Brachy_Intracavitary_LDR.51="51",
                                     Brachy_Intracavitary_HDR.52="52",
                                     Brachy_Interstitial_LDR.53="53",
                                     Brachy_Interstitial_HDR.54="54",
                                     Unknown.99 = "99"
                                        )
    new.d <- data.frame(new.d, radboostrxmodality)
    new.d <- apply_labels(new.d, radboostrxmodality = "rad_boost_rx_modality")
    #summary(new.d$radboostrxmodality)
temp.d <- data.frame (new.d.1, radboostrxmodality)
summarytools::view(dfSummary(new.d$radboostrxmodality, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 radboostrxmodality [labelled, factor] rad_boost_rx_modality 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR. 12. Brachy_Intracavitary_HDR. 13. Brachy_Interstitial_LDR.5 14. Brachy_Interstitial_HDR.5 15. Unknown.99
0(0.0%)
21(4.4%)
33(6.9%)
3(0.6%)
2(0.4%)
1(0.2%)
182(38.3%)
2(0.4%)
5(1.1%)
12(2.5%)
8(1.7%)
6(1.3%)
142(29.9%)
24(5.1%)
34(7.2%)
 3082 (86.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR.51 12. Brachy_Intracavitary_HDR.52 13. Brachy_Interstitial_LDR.53 14. Brachy_Interstitial_HDR.54 15. Unknown.99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR.51 12. Brachy_Intracavitary_HDR.52 13. Brachy_Interstitial_LDR.53 14. Brachy_Interstitial_HDR.54 15. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR.51 12. Brachy_Intracavitary_HDR.52 13. Brachy_Interstitial_LDR.53 14. Brachy_Interstitial_HDR.54 15. Unknown.99 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR.51 12. Brachy_Intracavitary_HDR.52 13. Brachy_Interstitial_LDR.53 14. Brachy_Interstitial_HDR.54 15. Unknown.99 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR. 12. Brachy_Intracavitary_HDR. 13. Brachy_Interstitial_LDR.5 14. Brachy_Interstitial_HDR.5 15. Unknown.99
0(0.0%)
13(10.7%)
10(8.2%)
3(2.5%)
1(0.8%)
0(0.0%)
51(41.8%)
0(0.0%)
5(4.1%)
4(3.3%)
0(0.0%)
1(0.8%)
1(0.8%)
6(4.9%)
27(22.1%)
 463 (79.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR. 12. Brachy_Intracavitary_HDR. 13. Brachy_Interstitial_LDR.5 14. Brachy_Interstitial_HDR.5 15. Unknown.99
0(0.0%)
8(2.3%)
23(6.5%)
0(0.0%)
1(0.3%)
1(0.3%)
131(37.1%)
2(0.6%)
0(0.0%)
8(2.3%)
8(2.3%)
5(1.4%)
141(39.9%)
18(5.1%)
7(2.0%)
 1401 (79.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_boost_tx.0 2. External_beam_NOS.20 3. Photons_6_10_MV.24 4. Photons_11_19_MV.25 5. Photons_mixed.27 6. Electrons.28 7. IMRT.31 8. Conformal_or_3D.32 9. Protons.40 10. Brachytherapy_NOS.50 11. Brachy_Intracavitary_LDR.51 12. Brachy_Intracavitary_HDR.52 13. Brachy_Interstitial_LDR.53 14. Brachy_Interstitial_HDR.54 15. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RAD–BOOST DOSE CGY

  • Description: Records the additional dose delivered to that part of the treatment volume encompassed by the boost fields or devices. The unit of measure is centiGray (cGy).

  • Rationale: To evaluate patterns of radiation oncology care, it is necessary to describe the boost radiation dose. A boost dose is administered to a volume within the regional volume. As in chemotherapy, outcomes are strongly related to the dose delivered.

  • Codes

    • (Fill blanks) Record the actual boost dose delivered
    • 00000 Boost radiation therapy was not administered
    • 88888 Not applicable, brachytherapy or radioisotopes administered to the patient
    • 99999 Boost radiation therapy administered, boost dose unknown

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3210


All data

st_css() #IMPORTANT!
  radboostdosecgy <- as.factor(d[,"radboostdosecgy"])
  
    new.d <- data.frame(new.d, radboostdosecgy)
    new.d <- apply_labels(new.d, radboostdosecgy = "rad_boost_dose_cgy")
    #summary(new.d$radboostdosecgy)
temp.d <- data.frame (new.d.1, radboostdosecgy)
summarytools::view(dfSummary(new.d$radboostdosecgy, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 radboostdosecgy [labelled, factor] rad_boost_dose_cgy No levels defined All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX DATE SYSTEMIC

  • Description: Date of initiation of systemic therapy that is part of the first course of treatment. Systemic therapy includes the administration of chemotherapy agents, hormone agents, biological response modifiers, bone marrow transplants, stem cell harvests, and surgical and/or radiation endocrine therapy. See Chapter X for date format. Use RX DATE SYSTEMIC FLAG [3231] if there is no appropriate or known date for this item.

Formerly RX Date–Systemic.


All data

  rxdatesystemic <- trimws(d[,"rxdatesystemic"])
  #new.d.n <- data.frame(new.d.n, rxdatesystemic) # keep NAACCR coding
  
  select99 <- ifelse(is.na(rxdatesystemic), F, substr(rxdatesystemic, start=7, stop=8)=="99")
  rxdatesystemic[select99] <- substr(rxdatesystemic[select99], start=1, stop=6)
  
  select6 <- ifelse(is.na(rxdatesystemic), F, nchar(trimws(rxdatesystemic))==6)
  rxdatesystemic[select6] <- paste(rxdatesystemic[select6], "15", sep="")
  
  select4 <- ifelse(is.na(rxdatesystemic), F, nchar(trimws(rxdatesystemic))==4)
  rxdatesystemic[select4] <- paste(rxdatesystemic[select4], "0615", sep="")
  
  rxdatesystemic <- as.Date(rxdatesystemic, c("%Y%m%d"))
  new.d <- data.frame(new.d, rxdatesystemic)
  new.d <- apply_labels(new.d, rxdatesystemic = "rx_date_systemic")
  temp.d <- data.frame (new.d.1, rxdatesystemic)
summarytools::view(dfSummary(new.d$rxdatesystemic, style = 'grid', max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxdatesystemic [labelled, Date] rx_date_systemic min : 2013-03-15 med : 2016-07-28 max : 2019-04-15 range : 6y 1m 0d 354 distinct values 2850 (80.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] 1. 2015-01-15 2. 2015-04-15 3. 2015-05-15 4. 2015-09-15 5. 2015-11-15 6. 2016-01-15 7. 2016-02-15 8. 2016-03-15 9. 2016-04-15 10. 2016-05-15 11. 2016-06-15 12. 2016-07-15 13. 2016-08-15 14. 2016-09-15 15. 2016-10-15 16. 2016-12-15 17. 2017-01-15 18. 2017-02-15 19. 2017-03-15 20. 2017-04-15 21. 2017-05-15 22. 2017-06-15 23. 2017-07-15 24. 2017-09-15 25. 2017-10-15 26. 2017-11-15 27. 2017-12-15 28. 2018-01-15
1(1.4%)
2(2.8%)
2(2.8%)
1(1.4%)
2(2.8%)
2(2.8%)
1(1.4%)
1(1.4%)
4(5.6%)
1(1.4%)
7(9.7%)
3(4.2%)
3(4.2%)
5(6.9%)
4(5.6%)
6(8.3%)
2(2.8%)
2(2.8%)
2(2.8%)
1(1.4%)
1(1.4%)
4(5.6%)
1(1.4%)
3(4.2%)
4(5.6%)
2(2.8%)
3(4.2%)
2(2.8%)
 249 (77.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] 1. 2015-02-15 2. 2015-04-15 3. 2015-05-15 4. 2015-06-15 5. 2015-08-15 6. 2015-09-15 7. 2015-10-15 8. 2015-11-15 9. 2015-12-15 10. 2016-01-15 11. 2016-02-15 12. 2016-03-15 13. 2016-04-15 14. 2016-05-15 15. 2016-06-15 16. 2016-08-15 17. 2016-09-15 18. 2016-10-15 19. 2016-11-15 20. 2016-12-15 21. 2017-02-15 22. 2017-03-15 23. 2017-04-15 24. 2017-05-15 25. 2017-06-15 26. 2017-07-15 27. 2017-12-15 28. 2018-01-15
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
3(5.9%)
1(2.0%)
1(2.0%)
1(2.0%)
2(3.9%)
2(3.9%)
4(7.8%)
3(5.9%)
3(5.9%)
1(2.0%)
2(3.9%)
1(2.0%)
3(5.9%)
3(5.9%)
4(7.8%)
1(2.0%)
2(3.9%)
1(2.0%)
2(3.9%)
1(2.0%)
3(5.9%)
1(2.0%)
 159 (75.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] 1. 2015-02-15 2. 2015-03-15 3. 2015-04-15 4. 2015-05-15 5. 2015-06-15 6. 2015-08-15 7. 2015-09-15 8. 2015-10-15 9. 2015-11-15 10. 2015-12-15 11. 2016-01-15 12. 2016-02-15 13. 2016-03-15 14. 2016-04-15 15. 2016-05-15 16. 2016-06-15 17. 2016-07-15 18. 2016-08-15 19. 2016-09-15 20. 2016-10-15 21. 2016-11-15 22. 2017-01-15 23. 2017-02-15 24. 2017-03-15 25. 2017-04-15 26. 2017-05-15
1(1.6%)
1(1.6%)
2(3.3%)
3(4.9%)
1(1.6%)
3(4.9%)
5(8.2%)
3(4.9%)
2(3.3%)
1(1.6%)
3(4.9%)
1(1.6%)
3(4.9%)
2(3.3%)
2(3.3%)
4(6.6%)
4(6.6%)
2(3.3%)
3(4.9%)
5(8.2%)
3(4.9%)
1(1.6%)
1(1.6%)
1(1.6%)
1(1.6%)
3(4.9%)
 254 (80.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] 1. 2013-03-15 2. 2013-05-15 3. 2013-06-15 4. 2013-08-15 5. 2014-08-15 6. 2014-09-15 7. 2015-01-15 8. 2015-02-15 9. 2015-03-15 10. 2015-04-15 11. 2015-06-15 12. 2015-07-15 13. 2015-08-15 14. 2015-09-15 15. 2015-10-15 16. 2015-11-15 17. 2015-12-15 18. 2016-02-15 19. 2016-03-15 20. 2016-07-15 21. 2016-08-15 22. 2016-10-15 23. 2016-11-15 24. 2016-12-15 25. 2017-04-15 26. 2017-05-15 27. 2017-06-15 28. 2017-07-15 29. 2017-08-15 30. 2017-09-15 31. 2017-11-15 32. 2017-12-15 33. 2018-01-15 34. 2018-03-15 35. 2018-04-15 36. 2018-05-15 37. 2018-06-15 38. 2018-07-15 39. 2018-08-15 40. 2018-09-15 41. 2018-10-15 42. 2018-11-15 43. 2018-12-15 44. 2019-02-15 45. 2019-04-15
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
4(4.9%)
3(3.7%)
2(2.4%)
2(2.4%)
1(1.2%)
3(3.7%)
2(2.4%)
2(2.4%)
2(2.4%)
2(2.4%)
5(6.1%)
1(1.2%)
2(2.4%)
2(2.4%)
1(1.2%)
2(2.4%)
5(6.1%)
1(1.2%)
2(2.4%)
2(2.4%)
1(1.2%)
3(3.7%)
4(4.9%)
3(3.7%)
1(1.2%)
1(1.2%)
3(3.7%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
 274 (77.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] min : 2015-02-10 med : 2016-09-23 max : 2019-02-28 range : 4y 0m 18d 130 distinct values 439 (75.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] min : 2015-01-16 med : 2016-06-15 max : 2018-05-29 range : 3y 4m 13d 231 distinct values 1460 (83.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rx_date_systemic [Date] 1. 2013-04-15
1(100.0%)
 15 (93.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


RX SUMM–TRANSPLNT/ENDOCR

  • Description: Identifies systemic therapeutic procedures administered as part of the first course of treatment at this and all other facilities. If none of these procedures were administered then this item records the reason they were not performed. These include bone marrow transplants, stem cell harvests, surgical and/or radiation endocrine therapy.

  • Rationale: This data item allows the evaluation of patterns of treatment, which involve the alteration of the immune system or change the patient’s response to tumor cells but do not involve the administration of antineoplastic agents.

  • Codes

    • 00 No transplant procedure or endocrine therapy was administered as part of first course therapy; diagnosed at autopsy
    • 10 Bone marrow transplant procedure was administered, but the type was not specified.
    • 11 Bone marrow transplant-autologous
    • 12 Bone marrow transplant-allogeneic
    • 20 Stem cell harvest and infusion
    • 30 Endocrine surgery and/or endocrine radiation therapy.
    • 40 Combination of endocrine surgery and/or radiation with a transplant procedure. (combination of codes 30 and 10, 11, 12 or 20).
    • 82 Hematologic transplant and/or endocrine surgery/radiation was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
    • 85 Hematologic transplant and/or endocrine surgery/radiation was not administered because the patient died prior to planned or recommended therapy.
    • 86 Hematologic transplant and/or endocrine surgery/radiation was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
    • 87 Hematologic transplant and/or endocrine surgery/radiation was not administered; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian; refusal noted in patient record
    • 88 Hematologic transplant and/or endocrine surgery/radiation was recommended, but it is unknown if it was administered
    • 99 It is unknown whether hematologic transplant and/or endocrine surgery/radiation was recommended or administered because it is not stated in patient record; death certificate-only cases

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3250


All data

st_css() #IMPORTANT!
  rxsummtransplntendocr <- as.factor(d[,"rxsummtransplntendocr"])
  levels(rxsummtransplntendocr) <- list(No_administered.0="0",
                                     Endo_surg_RT.30 = "30",
                                     Recomm_no_admin.86="86",
                                     Unknown.99 = "99"
                                        )
    new.d <- data.frame(new.d, rxsummtransplntendocr)
    new.d <- apply_labels(new.d, rxsummtransplntendocr = "rx_summ_transplnt_endocr")
    #summary(new.d$rxsummtransplntendocr)
temp.d <- data.frame (new.d.1, rxsummtransplntendocr)
summarytools::view(dfSummary(new.d$rxsummtransplntendocr, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 rxsummtransplntendocr [labelled, factor] rx_summ_transplnt_endocr 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99
0(0.0%)
4(10.0%)
1(2.5%)
35(87.5%)
 3517 (98.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
 320 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 314 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99
0(0.0%)
0(0.0%)
0(0.0%)
34(100.0%)
 551 (94.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99
0(0.0%)
3(75.0%)
1(25.0%)
0(0.0%)
 1750 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 rad_boost_rx_modality [factor] 1. No_administered.0 2. Endo_surg_RT.30 3. Recomm_no_admin.86 4. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-7 T


All data

st_css() #IMPORTANT!
  derivedajcc7t <- as.factor(d[,"derivedajcc7t"])
  
    new.d <- data.frame(new.d, derivedajcc7t)
    new.d <- apply_labels(new.d, derivedajcc7t = "derived_ajcc_7t")
    #summary(new.d$derivedajcc7t)
temp.d <- data.frame (new.d.1, derivedajcc7t)
summarytools::view(dfSummary(new.d$derivedajcc7t, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc7t [labelled, factor] derived_ajcc_7t 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
16(0.7%)
8(0.3%)
1034(43.9%)
2(0.1%)
83(3.5%)
32(1.4%)
537(22.8%)
316(13.4%)
137(5.8%)
98(4.2%)
12(0.5%)
11(0.5%)
68(2.9%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
0(0.0%)
0(0.0%)
25(24.5%)
0(0.0%)
5(4.9%)
5(4.9%)
43(42.2%)
3(2.9%)
13(12.7%)
7(6.9%)
0(0.0%)
1(1.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
0(0.0%)
1(1.1%)
47(53.4%)
1(1.1%)
8(9.1%)
3(3.4%)
11(12.5%)
7(8.0%)
7(8.0%)
1(1.1%)
1(1.1%)
0(0.0%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
2(1.4%)
1(0.7%)
49(34.8%)
0(0.0%)
7(5.0%)
2(1.4%)
48(34.0%)
8(5.7%)
12(8.5%)
10(7.1%)
1(0.7%)
0(0.0%)
1(0.7%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
2(1.5%)
0(0.0%)
45(33.3%)
0(0.0%)
4(3.0%)
2(1.5%)
48(35.6%)
14(10.4%)
9(6.7%)
7(5.2%)
2(1.5%)
1(0.7%)
1(0.7%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
1(0.8%)
1(0.8%)
31(25.8%)
0(0.0%)
7(5.8%)
2(1.7%)
46(38.3%)
12(10.0%)
12(10.0%)
7(5.8%)
0(0.0%)
1(0.8%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
11(0.6%)
5(0.3%)
832(47.5%)
1(0.1%)
52(3.0%)
18(1.0%)
336(19.2%)
267(15.2%)
83(4.7%)
66(3.8%)
8(0.5%)
8(0.5%)
65(3.7%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7t [factor] 1. 120 2. 150 3. 180 4. 199 5. 210 6. 220 7. 230 8. 299 9. 310 10. 320 11. 399 12. 400 13. 999
0(0.0%)
0(0.0%)
5(31.2%)
0(0.0%)
0(0.0%)
0(0.0%)
5(31.2%)
5(31.2%)
1(6.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-7 N


All data

st_css() #IMPORTANT!
  derivedajcc7n <- as.factor(d[,"derivedajcc7n"])
  
    new.d <- data.frame(new.d, derivedajcc7n)
    new.d <- apply_labels(new.d, derivedajcc7n = "derived_ajcc_7n")
    #summary(new.d$derivedajcc7n)
temp.d <- data.frame (new.d.1, derivedajcc7n)
summarytools::view(dfSummary(new.d$derivedajcc7n, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc7n [labelled, factor] derived_ajcc_7n 1. 0 
·
2. 000 3. 100 4. 999
2004(85.1%)
217(9.2%)
77(3.3%)
56(2.4%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
94(92.2%)
0(0.0%)
8(7.8%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
85(96.6%)
0(0.0%)
2(2.3%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
127(90.1%)
0(0.0%)
11(7.8%)
3(2.1%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
57(42.2%)
71(52.6%)
5(3.7%)
2(1.5%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
111(92.5%)
3(2.5%)
5(4.2%)
1(0.8%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
1514(86.4%)
143(8.2%)
46(2.6%)
49(2.8%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7n [factor] 1. 0 
·
2. 000 3. 100 4. 999
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-7 M


All data

st_css() #IMPORTANT!
  derivedajcc7m <- as.factor(d[,"derivedajcc7m"])
  
    new.d <- data.frame(new.d, derivedajcc7m)
    new.d <- apply_labels(new.d, derivedajcc7m = "derived_ajcc_7m")
    #summary(new.d$derivedajcc7m)
temp.d <- data.frame (new.d.1, derivedajcc7m)
summarytools::view(dfSummary(new.d$derivedajcc7m, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc7m [labelled, factor] derived_ajcc_7m 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
2081(88.4%)
225(9.6%)
10(0.4%)
34(1.4%)
3(0.1%)
1(0.0%)
 1203 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
100(98.0%)
0(0.0%)
1(1.0%)
1(1.0%)
0(0.0%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
86(97.7%)
0(0.0%)
1(1.1%)
1(1.1%)
0(0.0%)
0(0.0%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
138(97.9%)
0(0.0%)
0(0.0%)
3(2.1%)
0(0.0%)
0(0.0%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
56(41.5%)
73(54.1%)
1(0.7%)
5(3.7%)
0(0.0%)
0(0.0%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
115(95.8%)
3(2.5%)
0(0.0%)
2(1.7%)
0(0.0%)
0(0.0%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
1570(89.6%)
149(8.5%)
7(0.4%)
22(1.3%)
3(0.2%)
1(0.1%)
 2 (0.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7m [factor] 1. 0 
·
2. 000 3. 110 4. 120 5. 130 6. 199
16(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED AJCC-7 STAGE GRP


All data

st_css() #IMPORTANT!
  derivedajcc7stagegrp <- as.factor(d[,"derivedajcc7stagegrp"])
  
    new.d <- data.frame(new.d, derivedajcc7stagegrp)
    new.d <- apply_labels(new.d, derivedajcc7stagegrp = "derived_ajcc_7_stage_grp")
    #summary(new.d$derivedajcc7stagegrp)
temp.d <- data.frame (new.d.1, derivedajcc7stagegrp)
summarytools::view(dfSummary(new.d$derivedajcc7stagegrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedajcc7stagegrp [labelled, factor] derived_ajcc_7_stage_grp 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
435(18.5%)
687(29.2%)
824(35.0%)
202(8.6%)
114(4.8%)
91(3.9%)
 1204 (33.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
4(3.9%)
24(23.5%)
50(49.0%)
16(15.7%)
8(7.8%)
0(0.0%)
 219 (68.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
29(33.0%)
23(26.1%)
24(27.3%)
8(9.1%)
3(3.4%)
1(1.1%)
 122 (58.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
23(16.3%)
20(14.2%)
67(47.5%)
16(11.3%)
13(9.2%)
2(1.4%)
 174 (55.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
15(11.1%)
36(26.7%)
58(43.0%)
14(10.4%)
10(7.4%)
2(1.5%)
 221 (62.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
3(2.5%)
15(12.5%)
77(64.2%)
16(13.3%)
8(6.7%)
1(0.8%)
 465 (79.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
356(20.3%)
565(32.3%)
542(31.0%)
131(7.5%)
72(4.1%)
85(4.9%)
 3 (0.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_ajcc_7_stage_grp [factor] 1. 100 2. 320 3. 330 4. 500 5. 700 6. 999
5(31.2%)
4(25.0%)
6(37.5%)
1(6.2%)
0(0.0%)
0(0.0%)
 0 (0.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SEER PATH STG GRP

  • Description: This data item is needed to store the results of the derived algorithmic calculation of Derived SEER Pathologic Stage Group.

  • Rationale: The SEER Program is developing an algorithm to calculate clinical and pathologic stage group based on their T, N, and M components and additional information as needed to calculate stage. For example, for thyroid, additional information is needed on histology and age to calculate stage. Once the T, N, and M are known an algorithm can assign the stage group instead of a registrar having to look up the stage. There are also provisions for a separate field for directly assigned stage group if the registrar prefers entering it.

  • Codes

    • 0 Stage 0
    • 0A Stage 0A
    • 0IS Stage 0is
    • 1 Stage I
    • 1A Stage IA
    • 1A1 Stage IA1
    • 1A2 Stage IA2
    • 1B Stage IB
    • 1B1 Stage IB1
    • 1B2 Stage IB2
    • 1C Stage IC
    • 1S Stage IS
    • 2 Stage II
    • 2A Stage IIA
    • 2A1 Stage IIA1
    • 2A2 Stage IIA2
    • 2B Stage IIB
    • 2C Stage IIC
    • 3 Stage III
    • 3A Stage IIIA
    • 3B Stage IIIB
    • 3C Stage IIIC
    • 3C1 Stage IIIC1
    • 3C2 Stage IIIC2
    • 4 Stage IV
    • 4A Stage IVA
    • 4A1 Stage IVA1
    • 4A2 Stage IVA2
    • 4B Stage IVB
    • 4C Stage IVC
    • OC Occult
    • 88 Not applicable
    • 99 Unknown
    • Blank Algorithm has not been run

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3605


All data

st_css() #IMPORTANT!
  derivedseerpathstggrp <- as.factor(d[,"derivedseerpathstggrp"])
  levels(derivedseerpathstggrp) <- list(Stage_I.1="1",
                        Stage_IIA.2A="2A",
                        Stage_IIB.2B="2B",
                        Stage_III.3="3",
                        Stage_IV.4="4",
                        Unknown.99="99")
    new.d <- data.frame(new.d, derivedseerpathstggrp)
    new.d <- apply_labels(new.d, derivedseerpathstggrp = "derived_seer_path_stg_grp")
    #summary(new.d$derivedseerpathstggrp)
temp.d <- data.frame (new.d.1, derivedseerpathstggrp)
summarytools::view(dfSummary(new.d$derivedseerpathstggrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedseerpathstggrp [labelled, factor] derived_seer_path_stg_grp 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_path_stg_grp [factor] 1. Stage_I.1 2. Stage_IIA.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SEER PATH STG GRP

  • Description: This data item is needed to store the results of the derived algorithmic calculation of Derived SEER Clinical Stage Group.

  • Rationale: The SEER Program is developing an algorithm to calculate clinical and pathologic stage group based on their T, N, and M components and additional information as needed to calculate stage. For example, for thyroid, additional information is needed on histology and age to calculate stage. Once the T, N, and M are known an algorithm can assign the stage group instead of a registrar having to look up the stage. There are also provisions for a separate field for directly assigned stage group if the registrar prefers entering it.

  • Codes

    • 0 Stage 0
    • 0A Stage 0A
    • 01S Stage 0is
    • 1 Stage I
    • 1A Stage IA
    • 1A1 Stage IA1
    • 1A2 Stage IA2
    • 1B Stage IB
    • 1B1 Stage IB1
    • 1C Stage IC
    • 1S Stage IS
    • 2 Stage 2
    • 2A Stage 2A
    • 2A1 Stage 2A1
    • 2A2 Stage 2A2
    • 2B Stage 2B
    • 2C Stage 2C
    • 3 Stage 3
    • 3A Stage 3A
    • 3B Stage 3B
    • 3C Stage 3C
    • 3C1 Stage 3C1
    • 3C2 Stage 3C2
    • 4 Stage 4
    • 4A Stage 4A
    • 4A1 Stage 4A1
    • 4A2 Stage 4A2
    • 4B Stage 4B
    • 4C Stage 4C
    • OC Stage OC
    • 88 Not applicable
    • 99 Unknown
    • Blank The algorithm has not been run

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3610


All data

st_css() #IMPORTANT!
  derivedseerclinstggrp <- as.factor(d[,"derivedseerclinstggrp"])
  levels(derivedseerclinstggrp) <- list(Stage_I.1="1",
                        Stage_2A.2A="2A",
                        Stage_2B.2B="2B",
                        Stage_3.3="3",
                        Stage_4.4="4",
                        Unknown.99="99")
    new.d <- data.frame(new.d, derivedseerclinstggrp)
    new.d <- apply_labels(new.d, derivedseerclinstggrp = "derived_seer_clin_stg_grp")
    #summary(new.d$derivedseerclinstggrp)
temp.d <- data.frame (new.d.1, derivedseerclinstggrp)
summarytools::view(dfSummary(new.d$derivedseerclinstggrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedseerclinstggrp [labelled, factor] derived_seer_clin_stg_grp 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 3557 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 356 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_clin_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_2B.2B 4. Stage_3.3 5. Stage_4.4 6. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SEER CMB STG GRP


  • Description: This data item is needed to store the results of the derived algorithmic calculation of SEER Combined Stage Group.

  • Rationale: Rationale for change proposal (potential benefits of change): The SEER Program is developing an algorithm to calculate clinical and pathologic stage group based on their T, N, and M components and additional information as needed to calculate stage. For example, for thyroid, additional information is needed on histology and age to calculate stage. Once the T, N, and M are known an algorithm can assign the stage group instead of a registrar having to look up the stage. There are also provisions for a separate field for directly assigned stage group if the registrar prefers entering it.

  • Codes

    • 0A Stage 0
    • 0IS Stage 0is
    • 1 Stage I
    • 1A Stage IA
    • 1A2 Stage IA2
    • 1B Stage IB
    • 1B1 Stage IB1
    • 1B2 Stage IB2
    • 1C Stage IC
    • 1S Stage IS
    • 2 Stage 2
    • 2A Stage 2A
    • 2A1 Stage
    • 2A2 Stage IIA2
    • 2B Stage IIB
    • 2C Stage IIC
    • 3 Stage III
    • 3A Stage IIIA
    • 3B Stage IIIB
    • 3C Stage IIIC
    • 3C1 Stage IIIC1
    • 3C2 Stage IIIC2
    • 4 Stage IV
    • 4A Stage IVA
    • 4A1 Stage IVA1
    • 4A2 Stage IV42
    • 4B Stage IVB
    • 4C Stage IV4C
    • OC Occult
    • 88 Not applicable
    • 99 Unknown
    • Blank The algorithm has not been run

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3614


All data

st_css() #IMPORTANT!
  derivedseercmbstggrp <- as.factor(trimws(d[,"derivedseercmbstggrp"]))
  # THIS CODING NEEDS TO BE CONFIRMED
  levels(derivedseercmbstggrp) <- list(Stage_I.1="1",
                                            Stage_2A.2A="2A",
                                          Stage_IIB.2B="2B",
                                          Stage_III.3="3",
                                          Stage_IV.4="4",
                                          Not_applicable.88="88",
                                          Unknown.99="99")
    new.d <- data.frame(new.d, derivedseercmbstggrp)
    new.d <- apply_labels(new.d, derivedseercmbstggrp = "Stage group based on their T, N, and M")
    #summary(new.d$derivedseercmbstggrp)
temp.d <- data.frame (new.d.1, derivedseercmbstggrp)
summarytools::view(dfSummary(new.d$derivedseercmbstggrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedseercmbstggrp [labelled, factor] Stage group based on their T, N, and M 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99
318(29.8%)
160(15.0%)
172(16.1%)
160(15.0%)
95(8.9%)
0(0.0%)
162(15.2%)
 2490 (70.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99
45(47.9%)
0(0.0%)
0(0.0%)
21(22.3%)
15(16.0%)
0(0.0%)
13(13.8%)
 227 (70.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99
24(19.7%)
42(34.4%)
28(23.0%)
16(13.1%)
9(7.4%)
0(0.0%)
3(2.5%)
 88 (41.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99
32(18.4%)
25(14.4%)
66(37.9%)
22(12.6%)
19(10.9%)
0(0.0%)
10(5.7%)
 141 (44.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99
21(13.1%)
53(33.1%)
49(30.6%)
20(12.5%)
11(6.9%)
0(0.0%)
6(3.8%)
 196 (55.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99
196(37.9%)
40(7.7%)
29(5.6%)
81(15.7%)
41(7.9%)
0(0.0%)
130(25.1%)
 1237 (70.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_cmb_stg_grp [factor] 1. Stage_I.1 2. Stage_2A.2A 3. Stage_IIB.2B 4. Stage_III.3 5. Stage_IV.4 6. Not_applicable.88 7. Unknown.99 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SEER COMBINED T

  • Description: This new data item is needed to store the results of the derived algorithmic calculation of Derived SEER Combined T.

  • Rationale: The SEER Program has collected data from 2004 on AJCC 6th T, N, M and stage and from 2010 on AJCC 7th T, N, M and stage based on algorithmic derivation from Collaborative Stage (CS) data. These data were based on combining information from both the clinical and pathologic into a combined (or ‘best’) derived T, N, M and stage group. SEER would like to continue to be able to derive a combined T, N, M and stage group in order to evaluate time trends in cancer incidence by stage. SEER is designing an algorithm to combine the clinical and pathologic information for T, N, and M into a derived combined T, N and M and then the combined T, N, and M and additional information as needed are used to derive a combined stage.

  • Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)

    • 88 Not applicable
    • Blank Not derived

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3616


All data

st_css() #IMPORTANT!
  derivedseercombinedt <- as.factor(d[,"derivedseercombinedt"])
  
    new.d <- data.frame(new.d, derivedseercombinedt)
    new.d <- apply_labels(new.d, derivedseercombinedt = "derived_seer_combined_t")
    #summary(new.d$derivedseercombinedt)
temp.d <- data.frame (new.d.1, derivedseercombinedt)
summarytools::view(dfSummary(new.d$derivedseercombinedt, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedseercombinedt [labelled, factor] derived_seer_combined_t 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
17(1.1%)
11(0.7%)
2(0.1%)
813(50.2%)
34(2.1%)
65(4.0%)
27(1.7%)
53(3.3%)
3(0.2%)
11(0.7%)
9(0.6%)
3(0.2%)
41(2.5%)
24(1.5%)
25(1.5%)
6(0.4%)
294(18.2%)
1(0.1%)
115(7.1%)
62(3.8%)
1(0.1%)
1(0.1%)
 1939 (54.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
1(0.5%)
2(0.9%)
0(0.0%)
89(41.6%)
3(1.4%)
13(6.1%)
5(2.3%)
11(5.1%)
1(0.5%)
0(0.0%)
3(1.4%)
2(0.9%)
6(2.8%)
3(1.4%)
4(1.9%)
0(0.0%)
48(22.4%)
0(0.0%)
16(7.5%)
7(3.3%)
0(0.0%)
0(0.0%)
 107 (33.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
0(0.0%)
1(0.8%)
0(0.0%)
60(50.8%)
0(0.0%)
13(11.0%)
1(0.8%)
7(5.9%)
0(0.0%)
1(0.8%)
0(0.0%)
1(0.8%)
1(0.8%)
0(0.0%)
4(3.4%)
0(0.0%)
12(10.2%)
0(0.0%)
14(11.9%)
3(2.5%)
0(0.0%)
0(0.0%)
 92 (43.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
0(0.0%)
2(1.2%)
0(0.0%)
56(32.6%)
7(4.1%)
9(5.2%)
2(1.2%)
8(4.7%)
0(0.0%)
1(0.6%)
2(1.2%)
0(0.0%)
3(1.7%)
0(0.0%)
3(1.7%)
0(0.0%)
51(29.7%)
1(0.6%)
21(12.2%)
5(2.9%)
0(0.0%)
1(0.6%)
 143 (45.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
1(0.6%)
1(0.6%)
1(0.6%)
69(43.1%)
8(5.0%)
6(3.8%)
5(3.1%)
5(3.1%)
0(0.0%)
1(0.6%)
0(0.0%)
0(0.0%)
2(1.2%)
2(1.2%)
2(1.2%)
1(0.6%)
29(18.1%)
0(0.0%)
15(9.4%)
12(7.5%)
0(0.0%)
0(0.0%)
 196 (55.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
15(1.6%)
5(0.5%)
1(0.1%)
539(56.5%)
16(1.7%)
24(2.5%)
14(1.5%)
22(2.3%)
2(0.2%)
8(0.8%)
4(0.4%)
0(0.0%)
29(3.0%)
19(2.0%)
12(1.3%)
5(0.5%)
154(16.1%)
0(0.0%)
49(5.1%)
35(3.7%)
1(0.1%)
0(0.0%)
 800 (45.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_t [factor] 1. c1 
·
2. c1A 
·
3. c1B 
·
4. c1C 
·
5. c2 
·
6. c2A 
·
7. c2B 
·
8. c2C 
·
9. c3 
·
10. c3A 
·
11. c3B 
·
12. c4 
·
13. cX 
·
14. p2 
·
15. p2A 
·
16. p2B 
·
17. p2C 
·
18. p3 
·
19. p3A 
·
20. p3B 
·
21. p4 
·
22. pX 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SEER COMBINED N

  • Description: This item is used to store the results of the source information selected for the derived algorithmic calculation of Combined T, N, and M.

  • Rationale: The SEER Program has collected data from 2004 on AJCC 6th T, N, M and stage and from 2010 on AJCC 7th T, N, M and stage based on algorithmic derivation from Collaborative Stage (CS) data. These data were based on combining information from both the clinical and pathologic into a combined (or ‘best’) derived T, N, M and stage group. SEER would like to continue to be able to derive a combined T, N, M and stage group in order to evaluate time trends in cancer incidence by stage. SEER is designing an algorithm to combine the clinical and pathologic information for T, N, and M into a derived combined T, N and M and then the combined T, N, and M and additional information as needed are used to derive a combined stage. These derived combined T, N, M and stage items need to be new data items.

  • Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)

    • 88 Not applicable
    • Blank Not derived

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3618


All data

st_css() #IMPORTANT!
  derivedseercombinedn <- as.factor(d[,"derivedseercombinedn"])
  
    new.d <- data.frame(new.d, derivedseercombinedn)
    new.d <- apply_labels(new.d, derivedseercombinedn = "derived_seer_combined_n")
    #summary(new.d$derivedseercombinedn)
temp.d <- data.frame (new.d.1, derivedseercombinedn)
summarytools::view(dfSummary(new.d$derivedseercombinedn, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedseercombinedn [labelled, factor] derived_seer_combined_n 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
1155(71.4%)
31(1.9%)
72(4.4%)
312(19.3%)
40(2.5%)
8(0.5%)
 1939 (54.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
158(73.8%)
3(1.4%)
6(2.8%)
41(19.2%)
5(2.3%)
1(0.5%)
 107 (33.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
83(70.3%)
6(5.1%)
1(0.8%)
26(22.0%)
2(1.7%)
0(0.0%)
 92 (43.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
89(51.7%)
10(5.8%)
6(3.5%)
59(34.3%)
6(3.5%)
2(1.2%)
 143 (45.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
107(66.9%)
1(0.6%)
5(3.1%)
39(24.4%)
8(5.0%)
0(0.0%)
 196 (55.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
718(75.3%)
11(1.2%)
54(5.7%)
147(15.4%)
19(2.0%)
5(0.5%)
 800 (45.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_n [factor] 1. c0 
·
2. c1 
·
3. cX 
·
4. p0 
·
5. p1 
·
6. pX 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


DERIVED SEER COMBINED M

  • Description: This item is used to store the results of the source information selected for the derived algorithmic calculation of Combined T, N, and M.

  • Rationale: The SEER Program has collected data from 2004 on AJCC 6th T, N, M and stage and from 2010 on AJCC 7th T, N, M and stage based on algorithmic derivation from Collaborative Stage (CS) data. These data were based on combining information from both the clinical and pathologic into a combined (or ‘best’) derived T, N, M and stage group. SEER would like to continue to be able to derive a combined T, N, M and stage group in order to evaluate time trends in cancer incidence by stage. SEER is designing an algorithm to combine the clinical and pathologic information for T, N, and M into a derived combined T, N and M and then the combined T, N, and M and additional information as needed are used to derive a combined stage. These derived combined T, N, M and stage items need to be new data items.

  • Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)

    • 88 Not applicable
    • Blank Not derived

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3620


All data

st_css() #IMPORTANT!
  derivedseercombinedm <- as.factor(d[,"derivedseercombinedm"])
  
    new.d <- data.frame(new.d, derivedseercombinedm)
    new.d <- apply_labels(new.d, derivedseercombinedm = "derived_seer_combined_m")
    #summary(new.d$derivedseercombinedm)
temp.d <- data.frame (new.d.1, derivedseercombinedm)
summarytools::view(dfSummary(new.d$derivedseercombinedm, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 derivedseercombinedm [labelled, factor] derived_seer_combined_m 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
1575(97.3%)
5(0.3%)
3(0.2%)
26(1.6%)
4(0.2%)
2(0.1%)
3(0.2%)
 1939 (54.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
206(96.3%)
1(0.5%)
0(0.0%)
6(2.8%)
0(0.0%)
1(0.5%)
0(0.0%)
 107 (33.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
114(96.6%)
0(0.0%)
0(0.0%)
3(2.5%)
1(0.8%)
0(0.0%)
0(0.0%)
 92 (43.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
162(94.2%)
1(0.6%)
1(0.6%)
3(1.7%)
3(1.7%)
0(0.0%)
2(1.2%)
 143 (45.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
157(98.1%)
0(0.0%)
0(0.0%)
3(1.9%)
0(0.0%)
0(0.0%)
0(0.0%)
 196 (55.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
936(98.1%)
3(0.3%)
2(0.2%)
11(1.2%)
0(0.0%)
1(0.1%)
1(0.1%)
 800 (45.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 derived_seer_combined_m [factor] 1. c0 
·
2. c1 
·
3. c1A 
·
4. c1B 
·
5. c1C 
·
6. p1A 
·
7. p1C 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 1

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3780


All data

st_css() #IMPORTANT!
  secondarydiagnosis1 <- as.factor(d[,"secondarydiagnosis1"])
  
    new.d <- data.frame(new.d, secondarydiagnosis1)
    new.d <- apply_labels(new.d, secondarydiagnosis1 = "secondary_diagnosis_1")
    #summary(new.d$secondarydiagnosis1)
temp.d <- data.frame (new.d.1, secondarydiagnosis1)
summarytools::view(dfSummary(new.d$secondarydiagnosis1, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis1 [labelled, factor] secondary_diagnosis_1 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
[ 27 others ]
955(69.6%)
178(13.0%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
12(0.9%)
2(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
2(0.1%)
11(0.8%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
73(5.3%)
1(0.1%)
5(0.4%)
1(0.1%)
2(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
2(0.1%)
1(0.1%)
5(0.4%)
2(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
85(6.2%)
 2185 (61.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
51. N179 
·
52. N183 
·
53. N184 
·
54. N189 
·
55. N281 
·
56. N3281 
·
57. N329 
·
58. N390 
·
59. N393000 60. N40 
·
61. N400 
·
62. N401 
·
63. N4010 
·
64. N411 
·
65. N503 
·
66. N529 
·
67. R195 
·
68. R339 
·
69. R761100 70. R972 
·
71. R9720 
·
72. R972000 73. R9721 
·
74. Z6834 
·
75. Z8042 
·
76. Z8589 
·
77. Z9012 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
51. N179 
·
52. N183 
·
53. N184 
·
54. N189 
·
55. N281 
·
56. N3281 
·
57. N329 
·
58. N390 
·
59. N393000 60. N40 
·
61. N400 
·
62. N401 
·
63. N4010 
·
64. N411 
·
65. N503 
·
66. N529 
·
67. R195 
·
68. R339 
·
69. R761100 70. R972 
·
71. R9720 
·
72. R972000 73. R9721 
·
74. Z6834 
·
75. Z8042 
·
76. Z8589 
·
77. Z9012 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
51. N179 
·
52. N183 
·
53. N184 
·
54. N189 
·
55. N281 
·
56. N3281 
·
57. N329 
·
58. N390 
·
59. N393000 60. N40 
·
61. N400 
·
62. N401 
·
63. N4010 
·
64. N411 
·
65. N503 
·
66. N529 
·
67. R195 
·
68. R339 
·
69. R761100 70. R972 
·
71. R9720 
·
72. R972000 73. R9721 
·
74. Z6834 
·
75. Z8042 
·
76. Z8589 
·
77. Z9012 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
[ 27 others ]
52(38.2%)
44(32.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
12(8.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.7%)
0(0.0%)
0(0.0%)
1(0.7%)
0(0.0%)
0(0.0%)
1(0.7%)
0(0.0%)
1(0.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(2.2%)
0(0.0%)
0(0.0%)
1(0.7%)
1(0.7%)
0(0.0%)
1(0.7%)
13(9.6%)
 220 (61.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
[ 27 others ]
78(25.2%)
81(26.1%)
1(0.3%)
1(0.3%)
1(0.3%)
0(0.0%)
0(0.0%)
1(0.3%)
9(2.9%)
2(0.6%)
1(0.3%)
0(0.0%)
0(0.0%)
1(0.3%)
1(0.3%)
8(2.6%)
1(0.3%)
1(0.3%)
0(0.0%)
1(0.3%)
1(0.3%)
0(0.0%)
47(15.2%)
0(0.0%)
4(1.3%)
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
0(0.0%)
1(0.3%)
1(0.3%)
2(0.6%)
1(0.3%)
2(0.6%)
2(0.6%)
1(0.3%)
0(0.0%)
0(0.0%)
1(0.3%)
0(0.0%)
50(16.1%)
 275 (47.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
[ 27 others ]
821(89.0%)
53(5.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
1(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
1(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
1(0.1%)
14(1.5%)
1(0.1%)
1(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.1%)
0(0.0%)
1(0.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
22(2.4%)
 832 (47.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_1 [factor] 1. 0 
·
2. 0000000 3. A419 
·
4. B182000 5. E039 
·
6. E089 
·
7. E1022 
·
8. E116500 9. E119 
·
10. E119000 11. E538 
·
12. E6601 
·
13. E6609 
·
14. E669 
·
15. E7800 
·
16. E785 
·
17. E785000 18. E8881 
·
19. G4733 
·
20. G473900 21. G8929 
·
22. H409 
·
23. I10 
·
24. I1000 
·
25. I100000 26. I110 
·
27. I120 
·
28. I129 
·
29. I2510 
·
30. I25810 
·
31. I420 
·
32. I442 
·
33. I5022 
·
34. I509 
·
35. I6523 
·
36. I714000 37. I82409 
·
38. I8500 
·
39. J189 
·
40. J449 
·
41. J4520 
·
42. J45909 
·
43. J9811 
·
44. K219 
·
45. K219000 46. K429 
·
47. K625 
·
48. K660 
·
49. M19012 
·
50. M1991 
·
[ 27 others ]
4(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 12 (75.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 2

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3782


All data

st_css() #IMPORTANT!
  secondarydiagnosis2 <- as.factor(d[,"secondarydiagnosis2"])
  
    new.d <- data.frame(new.d, secondarydiagnosis2)
    new.d <- apply_labels(new.d, secondarydiagnosis2 = "secondary_diagnosis_2")
    #summary(new.d$secondarydiagnosis2)
temp.d <- data.frame (new.d.1, secondarydiagnosis2)
summarytools::view(dfSummary(new.d$secondarydiagnosis2, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis2 [labelled, factor] secondary_diagnosis_2 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
[ 125 others ]
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
2(0.4%)
2(0.4%)
4(0.8%)
1(0.2%)
1(0.2%)
2(0.4%)
1(0.2%)
1(0.2%)
2(0.4%)
1(0.2%)
1(0.2%)
4(0.8%)
31(6.0%)
1(0.2%)
3(0.6%)
1(0.2%)
1(0.2%)
6(1.2%)
9(1.7%)
2(0.4%)
2(0.4%)
2(0.4%)
61(11.8%)
8(1.5%)
1(0.2%)
2(0.4%)
1(0.2%)
1(0.2%)
5(1.0%)
1(0.2%)
1(0.2%)
1(0.2%)
1(0.2%)
3(0.6%)
83(16.1%)
1(0.2%)
9(1.7%)
3(0.6%)
1(0.2%)
2(0.4%)
3(0.6%)
1(0.2%)
1(0.2%)
4(0.8%)
1(0.2%)
2(0.4%)
236(45.6%)
 3040 (85.5%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
51. I270 
·
52. I341 
·
53. I4891 
·
54. I5022 
·
55. I5042 
·
56. I509 
·
57. I700 
·
58. I7100 
·
59. I739 
·
60. I82509 
·
61. I878 
·
62. I958 
·
63. J0190 
·
64. J189 
·
65. J302 
·
66. J449 
·
67. J449000 68. J45 
·
69. J4590 
·
70. J45909 
·
71. J459090 72. J479 
·
73. J841000 74. J954 
·
75. J984 
·
76. K212 
·
77. K219 
·
78. K4090 
·
79. K409000 80. K429 
·
81. K429000 82. K5730 
·
83. K611 
·
84. K648 
·
85. K66 
·
86. K660 
·
87. K7460 
·
88. K768900 89. K769 
·
90. K802000 91. K862 
·
92. K913 
·
93. L732000 94. M069 
·
95. M1030 
·
96. M109 
·
97. M129 
·
98. M15 
·
99. M160 
·
100. M19 
·
101. M19011 
·
102. M1990 
·
103. M47812 
·
104. M480600 105. M513600 106. M5430 
·
107. N138 
·
108. N139 
·
109. N182 
·
110. N183 
·
111. N186 
·
112. N189 
·
113. N281 
·
114. N2889 
·
115. N289 
·
116. N3289 
·
117. N390 
·
118. N393 
·
119. N3941 
·
120. N3943 
·
121. N39498 
·
122. N40 
·
123. N40.0 
·
124. N400 
·
125. N40000 
·
126. N401 
·
127. N4010 
·
128. N402 
·
129. N411 
·
130. N4110 
·
131. N423 
·
132. N4232 
·
133. N4289 
·
134. N429 
·
135. N433 
·
136. N5201 
·
137. N5203 
·
138. N5231 
·
139. N528 
·
140. N529 
·
141. R001 
·
142. R030 
·
143. R060000 144. R1030 
·
145. R160000 146. R312 
·
147. R312000 148. R319 
·
149. R338 
·
150. R350 
·
151. R351 
·
152. R3510 
·
153. R3911 
·
154. R3915 
·
155. R399 
·
156. R51 
·
157. R5383 
·
158. R590 
·
159. R6882 
·
160. R7303 
·
161. R739000 162. R935 
·
163. R972 
·
164. R9720 
·
165. R972000 166. Z6841 
·
167. Z8042 
·
168. Z804200 169. Z86010 
·
170. Z87891 
·
171. Z8791 
·
172. Z8860 
·
173. Z9049 
·
174. Z941 
·
175. Z950 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
51. I270 
·
52. I341 
·
53. I4891 
·
54. I5022 
·
55. I5042 
·
56. I509 
·
57. I700 
·
58. I7100 
·
59. I739 
·
60. I82509 
·
61. I878 
·
62. I958 
·
63. J0190 
·
64. J189 
·
65. J302 
·
66. J449 
·
67. J449000 68. J45 
·
69. J4590 
·
70. J45909 
·
71. J459090 72. J479 
·
73. J841000 74. J954 
·
75. J984 
·
76. K212 
·
77. K219 
·
78. K4090 
·
79. K409000 80. K429 
·
81. K429000 82. K5730 
·
83. K611 
·
84. K648 
·
85. K66 
·
86. K660 
·
87. K7460 
·
88. K768900 89. K769 
·
90. K802000 91. K862 
·
92. K913 
·
93. L732000 94. M069 
·
95. M1030 
·
96. M109 
·
97. M129 
·
98. M15 
·
99. M160 
·
100. M19 
·
101. M19011 
·
102. M1990 
·
103. M47812 
·
104. M480600 105. M513600 106. M5430 
·
107. N138 
·
108. N139 
·
109. N182 
·
110. N183 
·
111. N186 
·
112. N189 
·
113. N281 
·
114. N2889 
·
115. N289 
·
116. N3289 
·
117. N390 
·
118. N393 
·
119. N3941 
·
120. N3943 
·
121. N39498 
·
122. N40 
·
123. N40.0 
·
124. N400 
·
125. N40000 
·
126. N401 
·
127. N4010 
·
128. N402 
·
129. N411 
·
130. N4110 
·
131. N423 
·
132. N4232 
·
133. N4289 
·
134. N429 
·
135. N433 
·
136. N5201 
·
137. N5203 
·
138. N5231 
·
139. N528 
·
140. N529 
·
141. R001 
·
142. R030 
·
143. R060000 144. R1030 
·
145. R160000 146. R312 
·
147. R312000 148. R319 
·
149. R338 
·
150. R350 
·
151. R351 
·
152. R3510 
·
153. R3911 
·
154. R3915 
·
155. R399 
·
156. R51 
·
157. R5383 
·
158. R590 
·
159. R6882 
·
160. R7303 
·
161. R739000 162. R935 
·
163. R972 
·
164. R9720 
·
165. R972000 166. Z6841 
·
167. Z8042 
·
168. Z804200 169. Z86010 
·
170. Z87891 
·
171. Z8791 
·
172. Z8860 
·
173. Z9049 
·
174. Z941 
·
175. Z950 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
51. I270 
·
52. I341 
·
53. I4891 
·
54. I5022 
·
55. I5042 
·
56. I509 
·
57. I700 
·
58. I7100 
·
59. I739 
·
60. I82509 
·
61. I878 
·
62. I958 
·
63. J0190 
·
64. J189 
·
65. J302 
·
66. J449 
·
67. J449000 68. J45 
·
69. J4590 
·
70. J45909 
·
71. J459090 72. J479 
·
73. J841000 74. J954 
·
75. J984 
·
76. K212 
·
77. K219 
·
78. K4090 
·
79. K409000 80. K429 
·
81. K429000 82. K5730 
·
83. K611 
·
84. K648 
·
85. K66 
·
86. K660 
·
87. K7460 
·
88. K768900 89. K769 
·
90. K802000 91. K862 
·
92. K913 
·
93. L732000 94. M069 
·
95. M1030 
·
96. M109 
·
97. M129 
·
98. M15 
·
99. M160 
·
100. M19 
·
101. M19011 
·
102. M1990 
·
103. M47812 
·
104. M480600 105. M513600 106. M5430 
·
107. N138 
·
108. N139 
·
109. N182 
·
110. N183 
·
111. N186 
·
112. N189 
·
113. N281 
·
114. N2889 
·
115. N289 
·
116. N3289 
·
117. N390 
·
118. N393 
·
119. N3941 
·
120. N3943 
·
121. N39498 
·
122. N40 
·
123. N40.0 
·
124. N400 
·
125. N40000 
·
126. N401 
·
127. N4010 
·
128. N402 
·
129. N411 
·
130. N4110 
·
131. N423 
·
132. N4232 
·
133. N4289 
·
134. N429 
·
135. N433 
·
136. N5201 
·
137. N5203 
·
138. N5231 
·
139. N528 
·
140. N529 
·
141. R001 
·
142. R030 
·
143. R060000 144. R1030 
·
145. R160000 146. R312 
·
147. R312000 148. R319 
·
149. R338 
·
150. R350 
·
151. R351 
·
152. R3510 
·
153. R3911 
·
154. R3915 
·
155. R399 
·
156. R51 
·
157. R5383 
·
158. R590 
·
159. R6882 
·
160. R7303 
·
161. R739000 162. R935 
·
163. R972 
·
164. R9720 
·
165. R972000 166. Z6841 
·
167. Z8042 
·
168. Z804200 169. Z86010 
·
170. Z87891 
·
171. Z8791 
·
172. Z8860 
·
173. Z9049 
·
174. Z941 
·
175. Z950 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
[ 125 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.3%)
1(1.3%)
0(0.0%)
1(1.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.3%)
0(0.0%)
6(7.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.3%)
2(2.6%)
0(0.0%)
0(0.0%)
0(0.0%)
7(9.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(2.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
8(10.4%)
0(0.0%)
0(0.0%)
1(1.3%)
0(0.0%)
1(1.3%)
1(1.3%)
0(0.0%)
0(0.0%)
1(1.3%)
0(0.0%)
2(2.6%)
41(53.2%)
 279 (78.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
[ 125 others ]
1(0.5%)
0(0.0%)
1(0.5%)
1(0.5%)
1(0.5%)
0(0.0%)
3(1.4%)
0(0.0%)
0(0.0%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
0(0.0%)
0(0.0%)
4(1.9%)
9(4.3%)
1(0.5%)
2(1.0%)
1(0.5%)
1(0.5%)
4(1.9%)
3(1.4%)
0(0.0%)
1(0.5%)
1(0.5%)
28(13.4%)
5(2.4%)
1(0.5%)
1(0.5%)
0(0.0%)
1(0.5%)
2(1.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
42(20.1%)
1(0.5%)
7(3.3%)
1(0.5%)
0(0.0%)
0(0.0%)
1(0.5%)
0(0.0%)
1(0.5%)
1(0.5%)
1(0.5%)
0(0.0%)
78(37.3%)
 376 (64.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
[ 125 others ]
0(0.0%)
1(0.4%)
0(0.0%)
0(0.0%)
1(0.4%)
1(0.4%)
0(0.0%)
1(0.4%)
0(0.0%)
1(0.4%)
0(0.0%)
0(0.0%)
1(0.4%)
1(0.4%)
0(0.0%)
0(0.0%)
16(6.9%)
0(0.0%)
1(0.4%)
0(0.0%)
0(0.0%)
1(0.4%)
4(1.7%)
2(0.9%)
1(0.4%)
1(0.4%)
26(11.3%)
3(1.3%)
0(0.0%)
1(0.4%)
1(0.4%)
0(0.0%)
1(0.4%)
1(0.4%)
1(0.4%)
1(0.4%)
1(0.4%)
3(1.3%)
33(14.3%)
0(0.0%)
2(0.9%)
1(0.4%)
1(0.4%)
1(0.4%)
1(0.4%)
1(0.4%)
0(0.0%)
2(0.9%)
0(0.0%)
0(0.0%)
117(50.6%)
 1523 (86.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_2 [factor] 1. A419 
·
2. A6000 
·
3. B182000 4. B192 
·
5. B1920 
·
6. B20 
·
7. E039 
·
8. E081000 9. E1022 
·
10. E1122 
·
11. E1129 
·
12. E1140 
·
13. E1165 
·
14. E116500 15. E1169 
·
16. E118 
·
17. E119 
·
18. E1190 
·
19. E119000 20. E199 
·
21. E291 
·
22. E669 
·
23. E780 
·
24. E7800 
·
25. E782 
·
26. E782000 27. E785 
·
28. E785000 29. E835200 30. E876 
·
31. G40909 
·
32. G4730 
·
33. G4733 
·
34. G560 
·
35. G8918 
·
36. G9589 
·
37. H40 
·
38. H409 
·
39. I10 
·
40. I1000 
·
41. I100000 42. I110 
·
43. I119 
·
44. I120 
·
45. I129 
·
46. I129000 47. I251 
·
48. I2510 
·
49. I251000 50. I252 
·
51. I270 
·
52. I341 
·
53. I4891 
·
54. I5022 
·
55. I5042 
·
56. I509 
·
57. I700 
·
58. I7100 
·
59. I739 
·
60. I82509 
·
61. I878 
·
62. I958 
·
63. J0190 
·
64. J189 
·
65. J302 
·
66. J449 
·
67. J449000 68. J45 
·
69. J4590 
·
70. J45909 
·
71. J459090 72. J479 
·
73. J841000 74. J954 
·
75. J984 
·
76. K212 
·
77. K219 
·
78. K4090 
·
79. K409000 80. K429 
·
81. K429000 82. K5730 
·
83. K611 
·
84. K648 
·
85. K66 
·
86. K660 
·
87. K7460 
·
88. K768900 89. K769 
·
90. K802000 91. K862 
·
92. K913 
·
93. L732000 94. M069 
·
95. M1030 
·
96. M109 
·
97. M129 
·
98. M15 
·
99. M160 
·
100. M19 
·
101. M19011 
·
102. M1990 
·
103. M47812 
·
104. M480600 105. M513600 106. M5430 
·
107. N138 
·
108. N139 
·
109. N182 
·
110. N183 
·
111. N186 
·
112. N189 
·
113. N281 
·
114. N2889 
·
115. N289 
·
116. N3289 
·
117. N390 
·
118. N393 
·
119. N3941 
·
120. N3943 
·
121. N39498 
·
122. N40 
·
123. N40.0 
·
124. N400 
·
125. N40000 
·
126. N401 
·
127. N4010 
·
128. N402 
·
129. N411 
·
130. N4110 
·
131. N423 
·
132. N4232 
·
133. N4289 
·
134. N429 
·
135. N433 
·
136. N5201 
·
137. N5203 
·
138. N5231 
·
139. N528 
·
140. N529 
·
141. R001 
·
142. R030 
·
143. R060000 144. R1030 
·
145. R160000 146. R312 
·
147. R312000 148. R319 
·
149. R338 
·
150. R350 
·
151. R351 
·
152. R3510 
·
153. R3911 
·
154. R3915 
·
155. R399 
·
156. R51 
·
157. R5383 
·
158. R590 
·
159. R6882 
·
160. R7303 
·
161. R739000 162. R935 
·
163. R972 
·
164. R9720 
·
165. R972000 166. Z6841 
·
167. Z8042 
·
168. Z804200 169. Z86010 
·
170. Z87891 
·
171. Z8791 
·
172. Z8860 
·
173. Z9049 
·
174. Z941 
·
175. Z950 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 3

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3784


All data

st_css() #IMPORTANT!
  secondarydiagnosis3 <- as.factor(d[,"secondarydiagnosis3"])
  
    new.d <- data.frame(new.d, secondarydiagnosis3)
    new.d <- apply_labels(new.d, secondarydiagnosis3 = "secondary_diagnosis_3")
    #summary(new.d$secondarydiagnosis3)
temp.d <- data.frame (new.d.1, secondarydiagnosis3)
summarytools::view(dfSummary(new.d$secondarydiagnosis3, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis3 [labelled, factor] secondary_diagnosis_3 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
[ 106 others ]
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
17(5.1%)
3(0.9%)
1(0.3%)
3(0.9%)
1(0.3%)
1(0.3%)
5(1.5%)
1(0.3%)
1(0.3%)
6(1.8%)
2(0.6%)
3(0.9%)
1(0.3%)
2(0.6%)
25(7.6%)
3(0.9%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
4(1.2%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
4(1.2%)
1(0.3%)
1(0.3%)
30(9.1%)
1(0.3%)
2(0.6%)
1(0.3%)
1(0.3%)
6(1.8%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
1(0.3%)
180(54.4%)
 3226 (90.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
51. I5042 
·
52. I509 
·
53. I700000 54. I709000 55. I739 
·
56. I739000 57. I7789 
·
58. I82401 
·
59. I898 
·
60. I959 
·
61. J219 
·
62. J302 
·
63. J449 
·
64. J45 
·
65. J4590 
·
66. J45909 
·
67. J984 
·
68. K219 
·
69. K4020 
·
70. K4090 
·
71. K429 
·
72. K429000 73. K5730 
·
74. K5790 
·
75. K579000 76. K5900 
·
77. K603 
·
78. K644 
·
79. K7460 
·
80. K913 
·
81. L280 
·
82. L409 
·
83. L905 
·
84. M06862 
·
85. M069 
·
86. M10 
·
87. M109 
·
88. M1700 
·
89. M1990 
·
90. M199000 91. M47815 
·
92. M478160 93. M5441 
·
94. M546 
·
95. M549 
·
96. N138 
·
97. N170 
·
98. N179 
·
99. N181 
·
100. N183 
·
101. N183000 102. N189 
·
103. N281 
·
104. N318 
·
105. N319 
·
106. N323000 107. N390 
·
108. N3941 
·
109. N40 
·
110. N400 
·
111. N401 
·
112. N403 
·
113. N410 
·
114. N4232 
·
115. N4289 
·
116. N432 
·
117. N5201 
·
118. N529 
·
119. N9971 
·
120. N99842 
·
121. R05 
·
122. R200 
·
123. R3100 
·
124. R32 
·
125. R338 
·
126. R339 
·
127. R350 
·
128. R351 
·
129. R3911 
·
130. R3912 
·
131. R3915 
·
132. R5383 
·
133. R55 
·
134. R590 
·
135. R631 
·
136. R6889 
·
137. R748 
·
138. R918 
·
139. R9431 
·
140. R972 
·
141. R9720 
·
142. Z21 
·
143. Z6831 
·
144. Z801 
·
145. Z8546 
·
146. Z8673 
·
147. Z87891 
·
148. Z880 
·
149. Z886 
·
150. Z91013 
·
151. Z923 
·
152. Z9289 
·
153. Z955000 154. Z981 
·
155. Z98890 
·
156. Z992 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
51. I5042 
·
52. I509 
·
53. I700000 54. I709000 55. I739 
·
56. I739000 57. I7789 
·
58. I82401 
·
59. I898 
·
60. I959 
·
61. J219 
·
62. J302 
·
63. J449 
·
64. J45 
·
65. J4590 
·
66. J45909 
·
67. J984 
·
68. K219 
·
69. K4020 
·
70. K4090 
·
71. K429 
·
72. K429000 73. K5730 
·
74. K5790 
·
75. K579000 76. K5900 
·
77. K603 
·
78. K644 
·
79. K7460 
·
80. K913 
·
81. L280 
·
82. L409 
·
83. L905 
·
84. M06862 
·
85. M069 
·
86. M10 
·
87. M109 
·
88. M1700 
·
89. M1990 
·
90. M199000 91. M47815 
·
92. M478160 93. M5441 
·
94. M546 
·
95. M549 
·
96. N138 
·
97. N170 
·
98. N179 
·
99. N181 
·
100. N183 
·
101. N183000 102. N189 
·
103. N281 
·
104. N318 
·
105. N319 
·
106. N323000 107. N390 
·
108. N3941 
·
109. N40 
·
110. N400 
·
111. N401 
·
112. N403 
·
113. N410 
·
114. N4232 
·
115. N4289 
·
116. N432 
·
117. N5201 
·
118. N529 
·
119. N9971 
·
120. N99842 
·
121. R05 
·
122. R200 
·
123. R3100 
·
124. R32 
·
125. R338 
·
126. R339 
·
127. R350 
·
128. R351 
·
129. R3911 
·
130. R3912 
·
131. R3915 
·
132. R5383 
·
133. R55 
·
134. R590 
·
135. R631 
·
136. R6889 
·
137. R748 
·
138. R918 
·
139. R9431 
·
140. R972 
·
141. R9720 
·
142. Z21 
·
143. Z6831 
·
144. Z801 
·
145. Z8546 
·
146. Z8673 
·
147. Z87891 
·
148. Z880 
·
149. Z886 
·
150. Z91013 
·
151. Z923 
·
152. Z9289 
·
153. Z955000 154. Z981 
·
155. Z98890 
·
156. Z992 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
51. I5042 
·
52. I509 
·
53. I700000 54. I709000 55. I739 
·
56. I739000 57. I7789 
·
58. I82401 
·
59. I898 
·
60. I959 
·
61. J219 
·
62. J302 
·
63. J449 
·
64. J45 
·
65. J4590 
·
66. J45909 
·
67. J984 
·
68. K219 
·
69. K4020 
·
70. K4090 
·
71. K429 
·
72. K429000 73. K5730 
·
74. K5790 
·
75. K579000 76. K5900 
·
77. K603 
·
78. K644 
·
79. K7460 
·
80. K913 
·
81. L280 
·
82. L409 
·
83. L905 
·
84. M06862 
·
85. M069 
·
86. M10 
·
87. M109 
·
88. M1700 
·
89. M1990 
·
90. M199000 91. M47815 
·
92. M478160 93. M5441 
·
94. M546 
·
95. M549 
·
96. N138 
·
97. N170 
·
98. N179 
·
99. N181 
·
100. N183 
·
101. N183000 102. N189 
·
103. N281 
·
104. N318 
·
105. N319 
·
106. N323000 107. N390 
·
108. N3941 
·
109. N40 
·
110. N400 
·
111. N401 
·
112. N403 
·
113. N410 
·
114. N4232 
·
115. N4289 
·
116. N432 
·
117. N5201 
·
118. N529 
·
119. N9971 
·
120. N99842 
·
121. R05 
·
122. R200 
·
123. R3100 
·
124. R32 
·
125. R338 
·
126. R339 
·
127. R350 
·
128. R351 
·
129. R3911 
·
130. R3912 
·
131. R3915 
·
132. R5383 
·
133. R55 
·
134. R590 
·
135. R631 
·
136. R6889 
·
137. R748 
·
138. R918 
·
139. R9431 
·
140. R972 
·
141. R9720 
·
142. Z21 
·
143. Z6831 
·
144. Z801 
·
145. Z8546 
·
146. Z8673 
·
147. Z87891 
·
148. Z880 
·
149. Z886 
·
150. Z91013 
·
151. Z923 
·
152. Z9289 
·
153. Z955000 154. Z981 
·
155. Z98890 
·
156. Z992 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
[ 106 others ]
1(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
3(5.0%)
0(0.0%)
0(0.0%)
1(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(3.3%)
1(1.7%)
0(0.0%)
0(0.0%)
0(0.0%)
6(10.0%)
0(0.0%)
0(0.0%)
1(1.7%)
0(0.0%)
0(0.0%)
1(1.7%)
1(1.7%)
0(0.0%)
0(0.0%)
1(1.7%)
0(0.0%)
2(3.3%)
0(0.0%)
0(0.0%)
6(10.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.7%)
2(3.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
31(51.7%)
 296 (83.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
[ 106 others ]
0(0.0%)
1(0.8%)
0(0.0%)
0(0.0%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
7(5.3%)
2(1.5%)
0(0.0%)
1(0.8%)
1(0.8%)
1(0.8%)
2(1.5%)
1(0.8%)
1(0.8%)
1(0.8%)
0(0.0%)
1(0.8%)
1(0.8%)
0(0.0%)
13(9.8%)
3(2.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(0.8%)
2(1.5%)
0(0.0%)
0(0.0%)
1(0.8%)
0(0.0%)
1(0.8%)
0(0.0%)
1(0.8%)
0(0.0%)
8(6.1%)
0(0.0%)
2(1.5%)
0(0.0%)
0(0.0%)
1(0.8%)
1(0.8%)
1(0.8%)
0(0.0%)
1(0.8%)
0(0.0%)
70(53.0%)
 453 (77.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
[ 106 others ]
0(0.0%)
1(0.7%)
1(0.7%)
1(0.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(5.0%)
1(0.7%)
1(0.7%)
1(0.7%)
0(0.0%)
0(0.0%)
3(2.2%)
0(0.0%)
0(0.0%)
3(2.2%)
1(0.7%)
2(1.4%)
0(0.0%)
2(1.4%)
6(4.3%)
0(0.0%)
1(0.7%)
0(0.0%)
1(0.7%)
0(0.0%)
1(0.7%)
0(0.0%)
1(0.7%)
0(0.0%)
0(0.0%)
0(0.0%)
2(1.4%)
0(0.0%)
1(0.7%)
16(11.5%)
1(0.7%)
0(0.0%)
1(0.7%)
0(0.0%)
3(2.2%)
0(0.0%)
0(0.0%)
1(0.7%)
0(0.0%)
1(0.7%)
79(56.8%)
 1615 (92.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_3 [factor] 1. B1710 
·
2. B182 
·
3. B20 
·
4. E039 
·
5. E039000 6. E0590 
·
7. E079 
·
8. E0790 
·
9. E083212 10. E1169 
·
11. E119 
·
12. E119000 13. E291 
·
14. E6601 
·
15. E660100 16. E663 
·
17. E669 
·
18. E669000 19. E78 
·
20. E780 
·
21. E7800 
·
22. E782 
·
23. E782000 24. E784 
·
25. E785 
·
26. E785000 27. E875 
·
28. E876 
·
29. G44209 
·
30. G4700 
·
31. G4730 
·
32. G4731 
·
33. G4733 
·
34. G609000 35. G8929 
·
36. H3581 
·
37. H409 
·
38. H5441 
·
39. H548 
·
40. I10 
·
41. I1000 
·
42. I100000 43. I1129 
·
44. I129 
·
45. I2510 
·
46. I251000 47. I341 
·
48. I483 
·
49. I495 
·
50. I50 
·
51. I5042 
·
52. I509 
·
53. I700000 54. I709000 55. I739 
·
56. I739000 57. I7789 
·
58. I82401 
·
59. I898 
·
60. I959 
·
61. J219 
·
62. J302 
·
63. J449 
·
64. J45 
·
65. J4590 
·
66. J45909 
·
67. J984 
·
68. K219 
·
69. K4020 
·
70. K4090 
·
71. K429 
·
72. K429000 73. K5730 
·
74. K5790 
·
75. K579000 76. K5900 
·
77. K603 
·
78. K644 
·
79. K7460 
·
80. K913 
·
81. L280 
·
82. L409 
·
83. L905 
·
84. M06862 
·
85. M069 
·
86. M10 
·
87. M109 
·
88. M1700 
·
89. M1990 
·
90. M199000 91. M47815 
·
92. M478160 93. M5441 
·
94. M546 
·
95. M549 
·
96. N138 
·
97. N170 
·
98. N179 
·
99. N181 
·
100. N183 
·
101. N183000 102. N189 
·
103. N281 
·
104. N318 
·
105. N319 
·
106. N323000 107. N390 
·
108. N3941 
·
109. N40 
·
110. N400 
·
111. N401 
·
112. N403 
·
113. N410 
·
114. N4232 
·
115. N4289 
·
116. N432 
·
117. N5201 
·
118. N529 
·
119. N9971 
·
120. N99842 
·
121. R05 
·
122. R200 
·
123. R3100 
·
124. R32 
·
125. R338 
·
126. R339 
·
127. R350 
·
128. R351 
·
129. R3911 
·
130. R3912 
·
131. R3915 
·
132. R5383 
·
133. R55 
·
134. R590 
·
135. R631 
·
136. R6889 
·
137. R748 
·
138. R918 
·
139. R9431 
·
140. R972 
·
141. R9720 
·
142. Z21 
·
143. Z6831 
·
144. Z801 
·
145. Z8546 
·
146. Z8673 
·
147. Z87891 
·
148. Z880 
·
149. Z886 
·
150. Z91013 
·
151. Z923 
·
152. Z9289 
·
153. Z955000 154. Z981 
·
155. Z98890 
·
156. Z992 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 4

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3786


All data

st_css() #IMPORTANT!
  secondarydiagnosis4 <- as.factor(d[,"secondarydiagnosis4"])
  
    new.d <- data.frame(new.d, secondarydiagnosis4)
    new.d <- apply_labels(new.d, secondarydiagnosis4 = "secondary_diagnosis_4")
    #summary(new.d$secondarydiagnosis4)
temp.d <- data.frame (new.d.1, secondarydiagnosis4)
summarytools::view(dfSummary(new.d$secondarydiagnosis4, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis4 [labelled, factor] secondary_diagnosis_4 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
[ 68 others ]
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
2(0.9%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
12(5.7%)
2(0.9%)
1(0.5%)
1(0.5%)
5(2.4%)
1(0.5%)
3(1.4%)
14(6.6%)
1(0.5%)
1(0.5%)
1(0.5%)
3(1.4%)
3(1.4%)
1(0.5%)
1(0.5%)
15(7.1%)
1(0.5%)
1(0.5%)
2(0.9%)
1(0.5%)
3(1.4%)
2(0.9%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
7(3.3%)
1(0.5%)
2(0.9%)
1(0.5%)
2(0.9%)
2(0.9%)
8(3.8%)
1(0.5%)
1(0.5%)
1(0.5%)
1(0.5%)
92(43.4%)
 3345 (94.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
51. K648 
·
52. K649 
·
53. K660 
·
54. L2084 
·
55. L821 
·
56. M109 
·
57. M129 
·
58. M170 
·
59. M170000 60. M1712 
·
61. M19702 
·
62. M1990 
·
63. M1A 
·
64. M25512 
·
65. M25669 
·
66. M4722 
·
67. M478180 68. M47819 
·
69. M50321 
·
70. M5410 
·
71. M542 
·
72. M545000 73. N138 
·
74. N183 
·
75. N189 
·
76. N200 
·
77. N289 
·
78. N3289 
·
79. N359 
·
80. N40 
·
81. N400 
·
82. N401 
·
83. N411 
·
84. N521 
·
85. N528 
·
86. N529 
·
87. N8189 
·
88. R011 
·
89. R0781 
·
90. R140 
·
91. R2689 
·
92. R312 
·
93. R3129 
·
94. R338 
·
95. R350 
·
96. R351 
·
97. R3911 
·
98. R391100 99. R3915 
·
100. R52 
·
101. R590 
·
102. R634 
·
103. R6882 
·
104. R809 
·
105. R911 
·
106. R972 
·
107. R9720 
·
108. Z6822 
·
109. Z803 
·
110. Z8042 
·
111. Z853 
·
112. Z86010 
·
113. Z87891 
·
114. Z878910 115. Z91013 
·
116. Z95810 
·
117. Z9889 
·
118. Z992 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
51. K648 
·
52. K649 
·
53. K660 
·
54. L2084 
·
55. L821 
·
56. M109 
·
57. M129 
·
58. M170 
·
59. M170000 60. M1712 
·
61. M19702 
·
62. M1990 
·
63. M1A 
·
64. M25512 
·
65. M25669 
·
66. M4722 
·
67. M478180 68. M47819 
·
69. M50321 
·
70. M5410 
·
71. M542 
·
72. M545000 73. N138 
·
74. N183 
·
75. N189 
·
76. N200 
·
77. N289 
·
78. N3289 
·
79. N359 
·
80. N40 
·
81. N400 
·
82. N401 
·
83. N411 
·
84. N521 
·
85. N528 
·
86. N529 
·
87. N8189 
·
88. R011 
·
89. R0781 
·
90. R140 
·
91. R2689 
·
92. R312 
·
93. R3129 
·
94. R338 
·
95. R350 
·
96. R351 
·
97. R3911 
·
98. R391100 99. R3915 
·
100. R52 
·
101. R590 
·
102. R634 
·
103. R6882 
·
104. R809 
·
105. R911 
·
106. R972 
·
107. R9720 
·
108. Z6822 
·
109. Z803 
·
110. Z8042 
·
111. Z853 
·
112. Z86010 
·
113. Z87891 
·
114. Z878910 115. Z91013 
·
116. Z95810 
·
117. Z9889 
·
118. Z992 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
51. K648 
·
52. K649 
·
53. K660 
·
54. L2084 
·
55. L821 
·
56. M109 
·
57. M129 
·
58. M170 
·
59. M170000 60. M1712 
·
61. M19702 
·
62. M1990 
·
63. M1A 
·
64. M25512 
·
65. M25669 
·
66. M4722 
·
67. M478180 68. M47819 
·
69. M50321 
·
70. M5410 
·
71. M542 
·
72. M545000 73. N138 
·
74. N183 
·
75. N189 
·
76. N200 
·
77. N289 
·
78. N3289 
·
79. N359 
·
80. N40 
·
81. N400 
·
82. N401 
·
83. N411 
·
84. N521 
·
85. N528 
·
86. N529 
·
87. N8189 
·
88. R011 
·
89. R0781 
·
90. R140 
·
91. R2689 
·
92. R312 
·
93. R3129 
·
94. R338 
·
95. R350 
·
96. R351 
·
97. R3911 
·
98. R391100 99. R3915 
·
100. R52 
·
101. R590 
·
102. R634 
·
103. R6882 
·
104. R809 
·
105. R911 
·
106. R972 
·
107. R9720 
·
108. Z6822 
·
109. Z803 
·
110. Z8042 
·
111. Z853 
·
112. Z86010 
·
113. Z87891 
·
114. Z878910 115. Z91013 
·
116. Z95810 
·
117. Z9889 
·
118. Z992 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
[ 68 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
3(6.5%)
0(0.0%)
0(0.0%)
0(0.0%)
2(4.3%)
0(0.0%)
0(0.0%)
4(8.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
1(2.2%)
0(0.0%)
1(2.2%)
6(13.0%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
3(6.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.2%)
3(6.5%)
1(2.2%)
0(0.0%)
0(0.0%)
1(2.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
17(37.0%)
 310 (87.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
[ 68 others ]
0(0.0%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
0(0.0%)
1(1.2%)
0(0.0%)
1(1.2%)
2(2.5%)
2(2.5%)
0(0.0%)
0(0.0%)
1(1.2%)
0(0.0%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
0(0.0%)
1(1.2%)
1(1.2%)
1(1.2%)
0(0.0%)
3(3.8%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
2(2.5%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
4(5.0%)
0(0.0%)
1(1.2%)
1(1.2%)
1(1.2%)
2(2.5%)
4(5.0%)
1(1.2%)
0(0.0%)
0(0.0%)
1(1.2%)
37(46.2%)
 505 (86.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
[ 68 others ]
1(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.2%)
0(0.0%)
1(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
7(8.1%)
0(0.0%)
1(1.2%)
1(1.2%)
2(2.3%)
1(1.2%)
2(2.3%)
9(10.5%)
0(0.0%)
0(0.0%)
1(1.2%)
1(1.2%)
1(1.2%)
0(0.0%)
0(0.0%)
6(7.0%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.2%)
1(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.2%)
0(0.0%)
0(0.0%)
0(0.0%)
4(4.7%)
0(0.0%)
1(1.2%)
1(1.2%)
0(0.0%)
38(44.2%)
 1668 (95.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_4 [factor] 1. B122 
·
2. B181 
·
3. B1920 
·
4. B20 
·
5. E039 
·
6. E11321 
·
7. E1139 
·
8. E1159 
·
9. E1169 
·
10. E118000 11. E119 
·
12. E119000 13. E291 
·
14. E559 
·
15. E669 
·
16. E780 
·
17. E782 
·
18. E785 
·
19. E7850 
·
20. E871 
·
21. G4700 
·
22. G4730 
·
23. G4733 
·
24. G629 
·
25. G8929 
·
26. I10 
·
27. I100000 28. I119 
·
29. I129 
·
30. I251 
·
31. I2510 
·
32. I251000 33. I4891 
·
34. I503000 35. I639 
·
36. I639000 37. I723000 38. J3089 
·
39. J3489 
·
40. J449 
·
41. J4550 
·
42. J45909 
·
43. J9601 
·
44. J984 
·
45. K210000 46. K219 
·
47. K219000 48. K44900 
·
49. K562 
·
50. K5790 
·
51. K648 
·
52. K649 
·
53. K660 
·
54. L2084 
·
55. L821 
·
56. M109 
·
57. M129 
·
58. M170 
·
59. M170000 60. M1712 
·
61. M19702 
·
62. M1990 
·
63. M1A 
·
64. M25512 
·
65. M25669 
·
66. M4722 
·
67. M478180 68. M47819 
·
69. M50321 
·
70. M5410 
·
71. M542 
·
72. M545000 73. N138 
·
74. N183 
·
75. N189 
·
76. N200 
·
77. N289 
·
78. N3289 
·
79. N359 
·
80. N40 
·
81. N400 
·
82. N401 
·
83. N411 
·
84. N521 
·
85. N528 
·
86. N529 
·
87. N8189 
·
88. R011 
·
89. R0781 
·
90. R140 
·
91. R2689 
·
92. R312 
·
93. R3129 
·
94. R338 
·
95. R350 
·
96. R351 
·
97. R3911 
·
98. R391100 99. R3915 
·
100. R52 
·
101. R590 
·
102. R634 
·
103. R6882 
·
104. R809 
·
105. R911 
·
106. R972 
·
107. R9720 
·
108. Z6822 
·
109. Z803 
·
110. Z8042 
·
111. Z853 
·
112. Z86010 
·
113. Z87891 
·
114. Z878910 115. Z91013 
·
116. Z95810 
·
117. Z9889 
·
118. Z992 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 5

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3788


All data

st_css() #IMPORTANT!
  secondarydiagnosis5 <- as.factor(d[,"secondarydiagnosis5"])
  
    new.d <- data.frame(new.d, secondarydiagnosis5)
    new.d <- apply_labels(new.d, secondarydiagnosis5 = "secondary_diagnosis_5")
    #summary(new.d$secondarydiagnosis5)
temp.d <- data.frame (new.d.1, secondarydiagnosis5)
summarytools::view(dfSummary(new.d$secondarydiagnosis5, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis5 [labelled, factor] secondary_diagnosis_5 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
[ 34 others ]
1(0.8%)
1(0.8%)
2(1.5%)
1(0.8%)
4(3.1%)
4(3.1%)
1(0.8%)
6(4.6%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
2(1.5%)
1(0.8%)
5(3.8%)
1(0.8%)
3(2.3%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
3(2.3%)
2(1.5%)
1(0.8%)
7(5.3%)
1(0.8%)
1(0.8%)
1(0.8%)
4(3.1%)
1(0.8%)
1(0.8%)
4(3.1%)
1(0.8%)
1(0.8%)
2(1.5%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
1(0.8%)
46(35.1%)
 3426 (96.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
51. N40 
·
52. N400 
·
53. N400000 54. N401 
·
55. N411 
·
56. N423 
·
57. N486 
·
58. N509 
·
59. N528 
·
60. N529 
·
61. R0600 
·
62. R0602 
·
63. R200 
·
64. R300 
·
65. R3129 
·
66. R350 
·
67. R351 
·
68. R391500 69. R739 
·
70. R740 
·
71. R809 
·
72. R972 
·
73. Z6830 
·
74. Z6833 
·
75. Z8042 
·
76. Z85038 
·
77. Z86010 
·
78. Z87891 
·
79. Z9079 
·
80. Z9221 
·
81. Z940 
·
82. Z951 
·
83. Z96643 
·
84. Z9981 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
51. N40 
·
52. N400 
·
53. N400000 54. N401 
·
55. N411 
·
56. N423 
·
57. N486 
·
58. N509 
·
59. N528 
·
60. N529 
·
61. R0600 
·
62. R0602 
·
63. R200 
·
64. R300 
·
65. R3129 
·
66. R350 
·
67. R351 
·
68. R391500 69. R739 
·
70. R740 
·
71. R809 
·
72. R972 
·
73. Z6830 
·
74. Z6833 
·
75. Z8042 
·
76. Z85038 
·
77. Z86010 
·
78. Z87891 
·
79. Z9079 
·
80. Z9221 
·
81. Z940 
·
82. Z951 
·
83. Z96643 
·
84. Z9981 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
51. N40 
·
52. N400 
·
53. N400000 54. N401 
·
55. N411 
·
56. N423 
·
57. N486 
·
58. N509 
·
59. N528 
·
60. N529 
·
61. R0600 
·
62. R0602 
·
63. R200 
·
64. R300 
·
65. R3129 
·
66. R350 
·
67. R351 
·
68. R391500 69. R739 
·
70. R740 
·
71. R809 
·
72. R972 
·
73. Z6830 
·
74. Z6833 
·
75. Z8042 
·
76. Z85038 
·
77. Z86010 
·
78. Z87891 
·
79. Z9079 
·
80. Z9221 
·
81. Z940 
·
82. Z951 
·
83. Z96643 
·
84. Z9981 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
[ 34 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.1%)
1(3.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.1%)
1(3.1%)
0(0.0%)
1(3.1%)
1(3.1%)
0(0.0%)
0(0.0%)
2(6.2%)
1(3.1%)
0(0.0%)
1(3.1%)
0(0.0%)
0(0.0%)
1(3.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.1%)
0(0.0%)
1(3.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.1%)
1(3.1%)
0(0.0%)
1(3.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.1%)
1(3.1%)
0(0.0%)
0(0.0%)
1(3.1%)
0(0.0%)
1(3.1%)
0(0.0%)
0(0.0%)
12(37.5%)
 324 (91.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
[ 34 others ]
0(0.0%)
1(2.1%)
0(0.0%)
0(0.0%)
2(4.3%)
1(2.1%)
1(2.1%)
3(6.4%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.1%)
1(2.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.1%)
0(0.0%)
0(0.0%)
1(2.1%)
1(2.1%)
1(2.1%)
1(2.1%)
1(2.1%)
2(4.3%)
1(2.1%)
1(2.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.1%)
1(2.1%)
1(2.1%)
1(2.1%)
0(0.0%)
1(2.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.1%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.1%)
0(0.0%)
20(42.6%)
 538 (92.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
[ 34 others ]
1(1.9%)
0(0.0%)
2(3.8%)
1(1.9%)
1(1.9%)
2(3.8%)
0(0.0%)
3(5.8%)
1(1.9%)
0(0.0%)
0(0.0%)
1(1.9%)
0(0.0%)
0(0.0%)
2(3.8%)
0(0.0%)
2(3.8%)
0(0.0%)
3(5.8%)
0(0.0%)
1(1.9%)
0(0.0%)
0(0.0%)
1(1.9%)
0(0.0%)
0(0.0%)
2(3.8%)
0(0.0%)
0(0.0%)
4(7.7%)
0(0.0%)
0(0.0%)
1(1.9%)
3(5.8%)
0(0.0%)
0(0.0%)
2(3.8%)
0(0.0%)
0(0.0%)
2(3.8%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.9%)
0(0.0%)
0(0.0%)
1(1.9%)
0(0.0%)
0(0.0%)
1(1.9%)
14(26.9%)
 1702 (97.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_5 [factor] 1. B009 
·
2. B182 
·
3. E039 
·
4. E042 
·
5. E119 
·
6. E669 
·
7. E784000 8. E785 
·
9. E790 
·
10. E890 
·
11. G4730 
·
12. G4733 
·
13. G629 
·
14. H3552 
·
15. H409 
·
16. I071 
·
17. I10 
·
18. I110 
·
19. I2510 
·
20. I252 
·
21. I420 
·
22. I489100 23. I739 
·
24. J309 
·
25. J40 
·
26. J441 
·
27. J449 
·
28. J45909 
·
29. J90 
·
30. K219 
·
31. K219000 32. K298000 33. K4390 
·
34. M109 
·
35. M19011 
·
36. M19701 
·
37. M1990 
·
38. M25122 
·
39. M25561 
·
40. M478150 41. M480200 42. M542 
·
43. M549 
·
44. M7581 
·
45. N138 
·
46. N182 
·
47. N183 
·
48. N189 
·
49. N2581 
·
50. N3289 
·
51. N40 
·
52. N400 
·
53. N400000 54. N401 
·
55. N411 
·
56. N423 
·
57. N486 
·
58. N509 
·
59. N528 
·
60. N529 
·
61. R0600 
·
62. R0602 
·
63. R200 
·
64. R300 
·
65. R3129 
·
66. R350 
·
67. R351 
·
68. R391500 69. R739 
·
70. R740 
·
71. R809 
·
72. R972 
·
73. Z6830 
·
74. Z6833 
·
75. Z8042 
·
76. Z85038 
·
77. Z86010 
·
78. Z87891 
·
79. Z9079 
·
80. Z9221 
·
81. Z940 
·
82. Z951 
·
83. Z96643 
·
84. Z9981 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 6

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3790


All data

st_css() #IMPORTANT!
  secondarydiagnosis6 <- as.factor(d[,"secondarydiagnosis6"])
  
    new.d <- data.frame(new.d, secondarydiagnosis6)
    new.d <- apply_labels(new.d, secondarydiagnosis6 = "secondary_diagnosis_6")
    #summary(new.d$secondarydiagnosis6)
temp.d <- data.frame (new.d.1, secondarydiagnosis6)
summarytools::view(dfSummary(new.d$secondarydiagnosis6, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis6 [labelled, factor] secondary_diagnosis_6 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
[ 9 others ]
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
4(4.7%)
5(5.9%)
1(1.2%)
2(2.4%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
7(8.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
2(2.4%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
1(1.2%)
3(3.5%)
1(1.2%)
1(1.2%)
14(16.5%)
 3472 (97.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
51. R599 
·
52. R911 
·
53. R9431 
·
54. R972 
·
55. Y658 
·
56. Z6837 
·
57. Z86718 
·
58. Z96652 
·
59. Z9989 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
51. R599 
·
52. R911 
·
53. R9431 
·
54. R972 
·
55. Y658 
·
56. Z6837 
·
57. Z86718 
·
58. Z96652 
·
59. Z9989 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
51. R599 
·
52. R911 
·
53. R9431 
·
54. R972 
·
55. Y658 
·
56. Z6837 
·
57. Z86718 
·
58. Z96652 
·
59. Z9989 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
[ 9 others ]
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
1(5.3%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
1(5.3%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
0(0.0%)
1(5.3%)
0(0.0%)
1(5.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.3%)
2(10.5%)
0(0.0%)
0(0.0%)
4(21.1%)
 337 (94.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
[ 9 others ]
1(3.2%)
0(0.0%)
1(3.2%)
1(3.2%)
1(3.2%)
0(0.0%)
0(0.0%)
1(3.2%)
2(6.5%)
1(3.2%)
1(3.2%)
0(0.0%)
0(0.0%)
1(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
3(9.7%)
1(3.2%)
0(0.0%)
1(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.2%)
0(0.0%)
1(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.2%)
1(3.2%)
0(0.0%)
1(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(3.2%)
0(0.0%)
1(3.2%)
1(3.2%)
1(3.2%)
0(0.0%)
1(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(19.4%)
 554 (94.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
[ 9 others ]
0(0.0%)
1(2.9%)
1(2.9%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
3(8.6%)
3(8.6%)
0(0.0%)
0(0.0%)
1(2.9%)
2(5.7%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
3(8.6%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
1(2.9%)
0(0.0%)
1(2.9%)
1(2.9%)
1(2.9%)
0(0.0%)
0(0.0%)
1(2.9%)
0(0.0%)
1(2.9%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(2.9%)
0(0.0%)
0(0.0%)
1(2.9%)
1(2.9%)
1(2.9%)
4(11.4%)
 1719 (98.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_6 [factor] 1. E041 
·
2. E1122 
·
3. E119 
·
4. E119000 5. E3914 
·
6. E669 
·
7. E780 
·
8. E7800 
·
9. E785 
·
10. E790 
·
11. E875 
·
12. F328 
·
13. G4730 
·
14. G473300 15. G629 
·
16. G8929 
·
17. H409 
·
18. I10 
·
19. I208 
·
20. I251 
·
21. I429 
·
22. I519 
·
23. I7090 
·
24. J309 
·
25. J45 
·
26. K219 
·
27. K3580 
·
28. K7460 
·
29. M109 
·
30. M199 
·
31. M1990 
·
32. M5030 
·
33. M545 
·
34. N136 
·
35. N183 
·
36. N184 
·
37. N189 
·
38. N40 
·
39. N400 
·
40. N419 
·
41. N433 
·
42. N523100 43. N529 
·
44. N644 
·
45. R0002 
·
46. R202 
·
47. R339 
·
48. R351 
·
49. R351000 50. R51 
·
51. R599 
·
52. R911 
·
53. R9431 
·
54. R972 
·
55. Y658 
·
56. Z6837 
·
57. Z86718 
·
58. Z96652 
·
59. Z9989 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 7

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3792


All data

st_css() #IMPORTANT!
  secondarydiagnosis7 <- as.factor(d[,"secondarydiagnosis7"])
  
    new.d <- data.frame(new.d, secondarydiagnosis7)
    new.d <- apply_labels(new.d, secondarydiagnosis7 = "secondary_diagnosis_7")
    #summary(new.d$secondarydiagnosis7)
temp.d <- data.frame (new.d.1, secondarydiagnosis7)
summarytools::view(dfSummary(new.d$secondarydiagnosis7, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis7 [labelled, factor] secondary_diagnosis_7 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
2(4.1%)
2(4.1%)
2(4.1%)
1(2.0%)
3(6.1%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
2(4.1%)
1(2.0%)
1(2.0%)
1(2.0%)
2(4.1%)
1(2.0%)
1(2.0%)
3(6.1%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
1(2.0%)
 3508 (98.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
0(0.0%)
1(8.3%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
0(0.0%)
1(8.3%)
0(0.0%)
2(16.7%)
1(8.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(8.3%)
1(8.3%)
0(0.0%)
0(0.0%)
 344 (96.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
1(5.9%)
0(0.0%)
1(5.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.9%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.9%)
1(5.9%)
0(0.0%)
0(0.0%)
1(5.9%)
1(5.9%)
0(0.0%)
1(5.9%)
0(0.0%)
1(5.9%)
1(5.9%)
0(0.0%)
1(5.9%)
0(0.0%)
1(5.9%)
0(0.0%)
1(5.9%)
1(5.9%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.9%)
0(0.0%)
1(5.9%)
0(0.0%)
0(0.0%)
1(5.9%)
0(0.0%)
 568 (97.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
1(5.0%)
1(5.0%)
0(0.0%)
1(5.0%)
3(15.0%)
1(5.0%)
0(0.0%)
1(5.0%)
1(5.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.0%)
0(0.0%)
1(5.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.0%)
0(0.0%)
1(5.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.0%)
0(0.0%)
0(0.0%)
1(5.0%)
2(10.0%)
1(5.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(5.0%)
 1734 (98.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_7 [factor] 1. E119 
·
2. E669 
·
3. E780 
·
4. E781 
·
5. E785 
·
6. E8352 
·
7. E869 
·
8. G473 
·
9. H409 
·
10. I2510 
·
11. I251000 12. I252000 13. I4891 
·
14. I771 
·
15. J181 
·
16. J189 
·
17. K219000 18. K5909 
·
19. M069 
·
20. M1710 
·
21. M542 
·
22. M791 
·
23. N2581 
·
24. N400 
·
25. N401 
·
26. N5201 
·
27. N529 
·
28. N529000 29. R05 
·
30. R310 
·
31. R7303 
·
32. R740 
·
33. R9720 
·
34. Y838 
·
35. Z6831 
·
36. Z8042 
·
37. Z86711 
·
38. Z87891 
·
39. Z885 
·
40. Z9049 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 8

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3794


All data

st_css() #IMPORTANT!
  secondarydiagnosis8 <- as.factor(d[,"secondarydiagnosis8"])
  
    new.d <- data.frame(new.d, secondarydiagnosis8)
    new.d <- apply_labels(new.d, secondarydiagnosis8 = "secondary_diagnosis_8")
    #summary(new.d$secondarydiagnosis8)
temp.d <- data.frame (new.d.1, secondarydiagnosis8)
summarytools::view(dfSummary(new.d$secondarydiagnosis8, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis8 [labelled, factor] secondary_diagnosis_8 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
1(3.6%)
1(3.6%)
2(7.1%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
2(7.1%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
1(3.6%)
 3529 (99.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
0(0.0%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(12.5%)
1(12.5%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
 348 (97.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
0(0.0%)
1(7.7%)
1(7.7%)
1(7.7%)
0(0.0%)
1(7.7%)
0(0.0%)
1(7.7%)
1(7.7%)
1(7.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(7.7%)
1(7.7%)
0(0.0%)
1(7.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(7.7%)
0(0.0%)
0(0.0%)
1(7.7%)
1(7.7%)
 572 (97.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
1(14.3%)
0(0.0%)
0(0.0%)
0(0.0%)
 1747 (99.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_8 [factor] 1. E291 
·
2. E291000 3. E669 
·
4. E669000 5. E785 
·
6. E876 
·
7. G893 
·
8. I10 
·
9. I100000 10. I129 
·
11. I509 
·
12. I714 
·
13. J189 
·
14. M419 
·
15. N281 
·
16. N400 
·
17. N4232 
·
18. R0602 
·
19. R319 
·
20. R350 
·
21. R5383 
·
22. R791 
·
23. Z6833 
·
24. Z6836 
·
25. Z87891 
·
26. Z9079 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 9

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3796


All data

st_css() #IMPORTANT!
  secondarydiagnosis9 <- as.factor(d[,"secondarydiagnosis9"])
  
    new.d <- data.frame(new.d, secondarydiagnosis9)
    new.d <- apply_labels(new.d, secondarydiagnosis9 = "secondary_diagnosis_9")
    #summary(new.d$secondarydiagnosis9)
temp.d <- data.frame (new.d.1, secondarydiagnosis9)
summarytools::view(dfSummary(new.d$secondarydiagnosis9, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis9 [labelled, factor] secondary_diagnosis_9 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
2(9.1%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
2(9.1%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
2(9.1%)
1(4.5%)
 3535 (99.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
2(33.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(16.7%)
0(0.0%)
0(0.0%)
1(16.7%)
0(0.0%)
0(0.0%)
0(0.0%)
1(16.7%)
1(16.7%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 350 (98.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
0(0.0%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
0(0.0%)
1(9.1%)
0(0.0%)
1(9.1%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
 574 (98.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(20.0%)
0(0.0%)
1(20.0%)
1(20.0%)
1(20.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(20.0%)
0(0.0%)
 1749 (99.7%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_9 [factor] 1. E669 
·
2. E785000 3. G458 
·
4. H2513 
·
5. H9190 
·
6. I2510 
·
7. M109 
·
8. M1990 
·
9. N179 
·
10. N390 
·
11. N400 
·
12. N529 
·
13. R350 
·
14. R61 
·
15. Z683700 16. Z878910 17. Z882 
·
18. Z9079 
·
19. Z9089 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


SECONDARY DIAGNOSIS 10

  • Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).

  • Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).

  • Codes

    • A00.0 - B99.9 infectious and parasitic diseases
    • E00.0 - E89.89 endocrine and metabolic diseases
    • G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
    • R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
    • T36.0 - T50.996 medical poisonings
    • Y62.0 - Y84.9 medical misadventures
    • Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
    • Z68.1 - Z68.54 BMI
    • Z80.0 - Z80.9 family history of malignant neoplasms
    • Z85.0 - Z86.03 personal history of malignant neoplasms
    • Z86.1 - Z99.89 other personal health status

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3798


All data

st_css() #IMPORTANT!
  secondarydiagnosis10 <- as.factor(d[,"secondarydiagnosis10"])
  
    new.d <- data.frame(new.d, secondarydiagnosis10)
    new.d <- apply_labels(new.d, secondarydiagnosis10 = "secondary_diagnosis_10")
    #summary(new.d$secondarydiagnosis10)
temp.d <- data.frame (new.d.1, secondarydiagnosis10)
summarytools::view(dfSummary(new.d$secondarydiagnosis10, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 secondarydiagnosis10 [labelled, factor] secondary_diagnosis_10 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
1(7.1%)
2(14.3%)
1(7.1%)
1(7.1%)
 3543 (99.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(50.0%)
0(0.0%)
0(0.0%)
 354 (99.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
1(12.5%)
0(0.0%)
1(12.5%)
1(12.5%)
1(12.5%)
0(0.0%)
1(12.5%)
0(0.0%)
0(0.0%)
1(12.5%)
1(12.5%)
0(0.0%)
1(12.5%)
 577 (98.6%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
0(0.0%)
1(25.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(25.0%)
1(25.0%)
0(0.0%)
0(0.0%)
1(25.0%)
0(0.0%)
 1750 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 secondary_diagnosis_10 [factor] 1. E039000 2. E785 
·
3. H33322 
·
4. I480 
·
5. J329 
·
6. M25552 
·
7. N183 
·
8. N281 
·
9. N401 
·
10. R079000 11. R351 
·
12. T461X 
·
13. Y836 
·
 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GLEASON PATTERNS CLINICAL


  • Description: Prostate cancers are graded using Gleason score or pattern. This data item represents the Gleason primary and secondary patterns from needle core biopsy or TURP.

  • Rationale: Gleason Patterns Clinical is a Registry Data Collection Variable for Clinical Stage for AJCC. This data item was previously collected as Prostate, CS SSF# 7

  • Codes

    • 11 Primary pattern 1, secondary pattern 1
    • 13 Primary pattern 1, secondary pattern 3
    • 23 Primary pattern 2, secondary pattern 3
    • 25 Primary pattern 2, secondary pattern 5
    • 33 Primary pattern 3, secondary pattern 3
    • 34 Primary pattern 3, secondary pattern 4
    • 35 Primary pattern 3, secondary pattern 5
    • 39 Primary pattern 3, secondary pattern unknown
    • 43 Primary pattern 4, secondary pattern 3
    • 44 Primary pattern 4, secondary pattern 4
    • 45 Primary pattern 4, secondary pattern 5
    • 53 Primary pattern 5, secondary pattern 3
    • 54 Primary pattern 5, secondary pattern 4
    • 55 Primary pattern 5, secondary pattern 5
    • X7 No needle core biopsy/TURP performed
    • X9 Not documented in medical record/Gleason Patterns Clinical not assessed or unknown if assessed Unknown whether TURP and/or Biopsy done

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3838

All data

st_css() #IMPORTANT!
  gleasonpatternsclinical <- as.factor(trimws(d[,"gleasonpatternsclinical"]))
  # THIS CODING NEEDS TO BE CONFIRMED
  levels(gleasonpatternsclinical) <- list(Primary_1_secondary_1.11="11",
                                          Primary_1_secondary_3.13="13",
                                          Primary_2_secondary_3.23="23",
                                          Primary_2_secondary_5.25="25",
                                          Primary_3_secondary_3.33="33",
                                          Primary_3_secondary_5.34="34",
                                          Primary_3_secondary_5.35="35", 
                                          Primary_3_secondary_unknown.39="39",
                                          Primary_4_secondary_3.43="43",
                                          Primary_4_secondary_4.44="44",
                                          Primary_4_secondary_5.45="45",
                                          Primary_5_secondary_3.53="53",
                                          Primary_5_secondary_4.54="54",
                                          Primary_5_secondary_5.55="55",
                                          No_needle_core_biopsy_TURP_performed.X7 = "X7",
                                          Not_documented.X9 = "X9"
                                          )
    new.d <- data.frame(new.d, gleasonpatternsclinical)
    new.d <- apply_labels(new.d, gleasonpatternsclinical = "Gleason primary and secondary patterns ")
    #summary(new.d$gleasonpatternsclinical)
temp.d <- data.frame (new.d.1, gleasonpatternsclinical)
summarytools::view(dfSummary(new.d$gleasonpatternsclinical, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gleasonpatternsclinical [labelled, factor] Gleason primary and secondary patterns 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unkno 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TUR 16. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
8(12.7%)
25(39.7%)
0(0.0%)
0(0.0%)
16(25.4%)
6(9.5%)
4(6.3%)
0(0.0%)
2(3.2%)
0(0.0%)
1(1.6%)
1(1.6%)
 3494 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unknown.39 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TURP_performed.X7 16. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unknown.39 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TURP_performed.X7 16. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unknown.39 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TURP_performed.X7 16. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unkno 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TUR 16. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(11.5%)
24(39.3%)
0(0.0%)
0(0.0%)
16(26.2%)
6(9.8%)
4(6.6%)
0(0.0%)
2(3.3%)
0(0.0%)
1(1.6%)
1(1.6%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unkno 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TUR 16. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unkno 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TUR 16. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_clinical [factor] 1. Primary_1_secondary_1.11 2. Primary_1_secondary_3.13 3. Primary_2_secondary_3.23 4. Primary_2_secondary_5.25 5. Primary_3_secondary_3.33 6. Primary_3_secondary_5.34 7. Primary_3_secondary_5.35 8. Primary_3_secondary_unknown.39 9. Primary_4_secondary_3.43 10. Primary_4_secondary_4.44 11. Primary_4_secondary_5.45 12. Primary_5_secondary_3.53 13. Primary_5_secondary_4.54 14. Primary_5_secondary_5.55 15. No_needle_core_biopsy_TURP_performed.X7 16. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GLEASON PATTERNS PATHOLOGICAL


  • Description: Prostate cancers are graded using Gleason score or pattern. This data item represents the Gleason primary and secondary patterns from prostatectomy or autopsy.

  • Rationale: Gleason Patterns Pathological is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 9.

  • Codes

    • 14 Primary pattern 1, secondary pattern 4
    • 15 Primary pattern 1, secondary pattern 5
    • 33 Primary pattern 3, secondary pattern 3
    • 34 Primary pattern 3, secondary pattern 4
    • 35 Primary pattern 3, secondary pattern 5
    • 41 Primary pattern 4, secondary pattern 1
    • 43 Primary pattern 4, secondary pattern 3
    • 44 Primary pattern 4, secondary pattern 4
    • 45 Primary pattern 4, secondary pattern 5
    • 53 Primary pattern 5, secondary pattern 3
    • 54 Primary pattern 5, secondary pattern 4
    • X6 Prostatectomy done, primary pattern unknown, secondary pattern unknown
    • X7 No prostatectomy/autopsy performed
    • X9 Not documented in medical record/Gleason Patterns Pathological not assessed or unknown if assessed Unknown if prostatectomy done

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3839


All data

st_css() #IMPORTANT!
  gleasonpatternspathological <- as.factor(trimws(d[,"gleasonpatternspathological"]))
  levels(gleasonpatternspathological) <- list(Primary_1_secondary_4.14="14",
                                          Primary_1_secondary_5.15="15",
                                          Primary_3_secondary_3.33="33",
                                          Primary_3_secondary_5.34="34",
                                          Primary_3_secondary_5.35="35", 
                                          Primary_4_secondary_1.41="41",
                                          Primary_4_secondary_3.43="43",
                                          Primary_4_secondary_4.44="44",
                                          Primary_4_secondary_5.45="45",
                                          Primary_5_secondary_3.53="53",
                                          Primary_5_secondary_4.54="54",
                                          Primary_unknown_secondary_unknown.X6="X6",
                                          No_prostatectomy_autopsy_performed.X6= "X6",
                                          Not_documented.X9 = "X9"
                                          )
    new.d <- data.frame(new.d, gleasonpatternspathological)
    new.d <- apply_labels(new.d, gleasonpatternspathological = "Gleason primary and secondary patterns ")
    #summary(new.d$gleasonpatternspathological)
temp.d <- data.frame (new.d.1, gleasonpatternspathological)
summarytools::view(dfSummary(new.d$gleasonpatternspathological, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gleasonpatternspathological [labelled, factor] Gleason primary and secondary patterns 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary 13. No_prostatectomy_autopsy_ 14. Not_documented.X9
0(0.0%)
0(0.0%)
2(9.1%)
11(50.0%)
0(0.0%)
0(0.0%)
5(22.7%)
1(4.5%)
2(9.1%)
1(4.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 3535 (99.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary_unknown.X6 13. No_prostatectomy_autopsy_performed.X6 14. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary_unknown.X6 13. No_prostatectomy_autopsy_performed.X6 14. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary_unknown.X6 13. No_prostatectomy_autopsy_performed.X6 14. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary 13. No_prostatectomy_autopsy_ 14. Not_documented.X9
0(0.0%)
0(0.0%)
2(9.1%)
11(50.0%)
0(0.0%)
0(0.0%)
5(22.7%)
1(4.5%)
2(9.1%)
1(4.5%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 334 (93.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary_unknown.X6 13. No_prostatectomy_autopsy_performed.X6 14. Not_documented.X9 All NA's 585 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary_unknown.X6 13. No_prostatectomy_autopsy_performed.X6 14. Not_documented.X9 All NA's 1754 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_patterns_pathologica [factor] 1. Primary_1_secondary_4.14 2. Primary_1_secondary_5.15 3. Primary_3_secondary_3.33 4. Primary_3_secondary_5.34 5. Primary_3_secondary_5.35 6. Primary_4_secondary_1.41 7. Primary_4_secondary_3.43 8. Primary_4_secondary_4.44 9. Primary_4_secondary_5.45 10. Primary_5_secondary_3.53 11. Primary_5_secondary_4.54 12. Primary_unknown_secondary_unknown.X6 13. No_prostatectomy_autopsy_performed.X6 14. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GLEASON SCORE CLINICAL


  • Description: This data item records the Gleason score based on adding the values for primary and secondary patterns in Needle Core Biopsy or TURP.

  • Rationale: Gleason Score Clinical is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 8.

  • Codes

    • 02 Gleason score 2
    • 03 Gleason score 3
    • 04 Gleason score 4
    • 05 Gleason score 5
    • 06 Gleason score 6
    • 07 Gleason score 7
    • 08 Gleason score 8
    • 09 Gleason score 9
    • 10 Gleason score 10
    • X7 No needle core biopsy/TURP performed
    • X8 Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
    • X9 Not documented in medical record/Gleason Score Clinical not assessed or unknown if assessed

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3840


All data

st_css() #IMPORTANT!
  gleasonscoreclinical <- trimws(d[,"gleasonscoreclinical"])
  gleasonscoreclinical[ which(gleasonscoreclinical=="06")]<-"6"
  gleasonscoreclinical[ which(gleasonscoreclinical=="07")]<-"7"
  gleasonscoreclinical[ which(gleasonscoreclinical=="08")]<-"8"
  gleasonscoreclinical[ which(gleasonscoreclinical=="09")]<-"9"
  gleasonscoreclinical<-as.factor(gleasonscoreclinical)
  levels(gleasonscoreclinical) <- list(Gleason_score_2.2="2",
                                       Gleason_score_3.3="3",
                                       Gleason_score_4.4="4",
                                       Gleason_score_5.5="5",
                                       Gleason_score_6.6="6", 
                                       Gleason_score_7.7="7",
                                       Gleason_score_8.8="8",
                                       Gleason_score_9.9="9",
                                       Gleason_score_10.10="10",
                                       Not_documented.X9 = "X9")
    new.d <- data.frame(new.d, gleasonscoreclinical)
    new.d <- apply_labels(new.d, gleasonscoreclinical = " Gleason score")
    #summary(new.d$gleasonscoreclinical)
temp.d <- data.frame (new.d.1, gleasonscoreclinical)
summarytools::view(dfSummary(new.d$gleasonscoreclinical, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE , headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gleasonscoreclinical [labelled, factor] Gleason score 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
7(11.1%)
35(55.6%)
5(7.9%)
5(7.9%)
0(0.0%)
11(17.5%)
 3494 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
6(9.8%)
34(55.7%)
5(8.2%)
5(8.2%)
0(0.0%)
11(18.0%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_clinical [factor] 1. Gleason_score_2.2 2. Gleason_score_3.3 3. Gleason_score_4.4 4. Gleason_score_5.5 5. Gleason_score_6.6 6. Gleason_score_7.7 7. Gleason_score_8.8 8. Gleason_score_9.9 9. Gleason_score_10.10 10. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GLEASON SCORE PATHOLOGICAL


  • Description: This data item records the Gleason score based on adding the values for primary and secondary patterns from prostatectomy or autopsy.

  • Rationale: Gleason Score Pathological is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 10.

  • Codes

    • 02 Gleason score 2
    • 03 Gleason score 3
    • 04 Gleason score 4
    • 05 Gleason score 5
    • 06 Gleason score 6
    • 07 Gleason score 7
    • 08 Gleason score 8
    • 09 Gleason score 9
    • 10 Gleason score 10
    • X7 No prostatectomy done
    • X8 Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
    • X9 Not documented in medical record/Gleason Score Pathological not assessed or unknown if assessed

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3841


All data

st_css() #IMPORTANT!
  gleasonscorepathological <- as.factor(trimws(d[,"gleasonscorepathological"]))
  levels(gleasonscorepathological) <- list(Gleason_score_3.03 ="03",
                                       Gleason_score_4.04="04",
                                       Gleason_score_6.06="06", 
                                       Gleason_score_7.07="07",
                                       Gleason_score_8.08="08",
                                       Gleason_score_9.09="09",
                                       No_prostatectomy_done.X7="X7",
                                       Not_applicable.X8="X8",
                                       Not_documented.X9 = "X9")
    new.d <- data.frame(new.d, gleasonscorepathological)
    new.d <- apply_labels(new.d, gleasonscorepathological = " Gleason score")
    #summary(new.d$gleasonscorepathological)
temp.d <- data.frame (new.d.1, gleasonscorepathological)
summarytools::view(dfSummary(new.d$gleasonscorepathological, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gleasonscorepathological [labelled, factor] Gleason score 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9
0(0.0%)
0(0.0%)
1(1.6%)
14(22.2%)
1(1.6%)
2(3.2%)
33(52.4%)
0(0.0%)
12(19.0%)
 3494 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9
0(0.0%)
0(0.0%)
1(1.6%)
14(23.0%)
1(1.6%)
2(3.3%)
31(50.8%)
0(0.0%)
12(19.7%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_score_pathological [factor] 1. Gleason_score_3.03 2. Gleason_score_4.04 3. Gleason_score_6.06 4. Gleason_score_7.07 5. Gleason_score_8.08 6. Gleason_score_9.09 7. No_prostatectomy_done.X7 8. Not_applicable.X8 9. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GLEASON TERTIARY PATTERN


  • Description: Prostate cancers are graded using Gleason score or pattern. This data item represents the tertiary pattern value from prostatectomy or autopsy.

  • Rationale: Tertiary Gleason pattern on prostatectomy is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 11.

  • Codes

    • 10 Tertiary pattern 1
    • 20 Tertiary pattern 2
    • 30 Tertiary pattern 3
    • 40 Tertiary pattern 4
    • 50 Tertiary pattern 5
    • X7 No prostatectomy/autopsy performed
    • X8 Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
    • X9 Not documented in medical record/Gleason Tertiary Pattern not assessed or unknown if assessed

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3842


All data

st_css() #IMPORTANT!
  gleasontertiarypattern <- as.factor(trimws(d[,"gleasontertiarypattern"]))
  levels(gleasontertiarypattern) <- list(Tertiary_pattern_2.20="20",
                                         Tertiary_pattern_3.30="30",
                                         Tertiary_pattern_4.40="40", 
                                         Tertiary_pattern_5.50="50",
                                         No_prostatectomy_autopsy_performed.X7="X7",
                                         Not_applicable.X8="X8",
                                         Not_documented.X9 = "X9")
    new.d <- data.frame(new.d, gleasontertiarypattern)
    new.d <- apply_labels(new.d, gleasontertiarypattern = " Gleason score")
    #summary(new.d$gleasontertiarypattern)
temp.d <- data.frame (new.d.1, gleasontertiarypattern)
summarytools::view(dfSummary(new.d$gleasontertiarypattern, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gleasontertiarypattern [labelled, factor] Gleason score 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_ 6. Not_applicable.X8 7. Not_documented.X9
0(0.0%)
1(1.6%)
0(0.0%)
1(1.6%)
34(54.0%)
0(0.0%)
27(42.9%)
 3494 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_performed.X7 6. Not_applicable.X8 7. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_performed.X7 6. Not_applicable.X8 7. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_performed.X7 6. Not_applicable.X8 7. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_ 6. Not_applicable.X8 7. Not_documented.X9
0(0.0%)
1(1.6%)
0(0.0%)
1(1.6%)
32(52.5%)
0(0.0%)
27(44.3%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_ 6. Not_applicable.X8 7. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_ 6. Not_applicable.X8 7. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 gleason_tertiary_pattern [factor] 1. Tertiary_pattern_2.20 2. Tertiary_pattern_3.30 3. Tertiary_pattern_4.40 4. Tertiary_pattern_5.50 5. No_prostatectomy_autopsy_performed.X7 6. Not_applicable.X8 7. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GRADE CLINICAL


  • Description: This data item records the grade of a solid primary tumor before any treatment (surgical resection or initiation of any treatment including neoadjuvant). For cases diagnosed January 1, 2018, and later, this data item, along with Grade Pathological and Grade Post-Neoadjuvant, replaces NAACCR Data Item Grade [440] as well as SSF’s for cancer sites with alternative grading systems (e.g., breast [Bloom-Richardson], prostate [Gleason]).

  • Rationale: Grade is a measure of the aggressiveness of the tumor. Grade and cell type are important prognostic indicators for many cancers. For some sites, grade is required to assign the clinical stage group. For those cases that are eligible AJCC staging, the recommended grading system is specified in the AJCC Chapter. The AJCC Chapter-specific grading systems (codes 1-5) take priority over the generic grade definitions (codes A-E, L, H, 9). For those cases that are not eligible for AJCC staging, if the recommended grading system is not documented, the generic grade definitions would apply.

  • Codes

    • 1 Grade Group 1: Gleason score less than or equal to 6
    • 2 Grade Group 2: Gleason score 7/Gleason pattern 3+4
    • 3 Grade Group 3: Gleason score 7/Gleason pattern 4+3
    • 4 Grade Group 4: Gleason score 8
    • 5 Grade Group 5: Gleason score 9 or 10
    • 9 Grade cannot be assessed; Unknown

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank. Leave blank for cases diagnosed prior to 2018.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3843


All data

st_css() #IMPORTANT!
  gradeclinical <- as.factor(trimws(d[,"gradeclinical"]))
  levels(gradeclinical) <- list(Grade_Group_1.1="1",
                                Grade_Group_2.2="2",
                                Grade_Group_3.3="3", 
                                Grade_Group_4.4="4",
                                Grade_Group_5.5="5",
                                Not_found_grade_8.8="8",
                                Unknown.9 = "9")
    new.d <- data.frame(new.d, gradeclinical)
    new.d <- apply_labels(new.d, gradeclinical = "Grade of primary tumor before any treatment")
    #summary(new.d$gradeclinical)
temp.d <- data.frame (new.d.1, gradeclinical)
summarytools::view(dfSummary(new.d$gradeclinical, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE , headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gradeclinical [labelled, factor] Grade of primary tumor before any treatment 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
8(12.5%)
25(39.1%)
15(23.4%)
7(10.9%)
6(9.4%)
0(0.0%)
3(4.7%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_clinical [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_clinical [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_clinical [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
7(11.5%)
24(39.3%)
15(24.6%)
7(11.5%)
6(9.8%)
0(0.0%)
2(3.3%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_clinical [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_clinical [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
1(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(50.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_clinical [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


GRADE PATHOLOGICAL


  • Description: This data item records the grade of a solid primary tumor that has been resected and for which no neoadjuvant therapy was administered. If AJCC staging is being assigned, the tumor must have met the surgical resection requirements in the AJCC manual. This may include the grade from the clinical workup. Record the highest grade documented from any microscopic specimen of the primary site whether from the clinical workup or the surgical resection. For cases diagnosed January 1, 2018, and later, this data item, along with Grade Clinical and Grade Post-Neoadjuvant, replaces NAACCR Data Item Grade [440] as well as SSF’s for cancer sites with alternative grading systems (e.g., breast [Bloom-Richardson], prostate [Gleason]).

  • Rationale: Grade is a measure of the aggressiveness of the tumor. Grade and cell type are important prognostic indicators for many cancers. For some sites, grade is required to assign the pathological stage group. For those cases that are eligible AJCC staging, the recommended grading system is specified in the AJCC Chapter. The AJCC Chapter-specific grading systems (codes 1-5) take priority over the generic grade definitions (codes A-E, L, H, 9). For those cases that are not eligible for AJCC staging, if the recommended grading system is not documented, the generic grade definitions would apply.

  • Codes

    • 1 Grade Group 1: Gleason score less than or equal to 6
    • 2 Grade Group 2: Gleason score 7/Gleason pattern 3+4
    • 3 Grade Group 3: Gleason score 7/Gleason pattern 4+3
    • 4 Grade Group 4: Gleason score 8
    • 5 Grade Group 5: Gleason score 9 or 10
    • 9 Grade cannot be assessed; Unknown

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank. Leave blank for cases diagnosed prior to 2018.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3844


All data

st_css() #IMPORTANT!
  gradepathological <- as.factor(trimws(d[,"gradepathological"]))
  levels(gradepathological) <- list(Grade_Group_1.1="1",
                                Grade_Group_2.2="2",
                                Grade_Group_3.3="3", 
                                Grade_Group_4.4="4",
                                Grade_Group_5.5="5",
                                Not_found_grade_8.8="8",
                                Unknown.9 = "9")
    new.d <- data.frame(new.d, gradepathological)
    new.d <- apply_labels(new.d, gradepathological = "Grade of primary tumor before any treatment")
    #summary(new.d$gradepathological)
temp.d <- data.frame (new.d.1, gradepathological)
summarytools::view(dfSummary(new.d$gradepathological, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 gradepathological [labelled, factor] Grade of primary tumor before any treatment 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
2(3.1%)
9(14.1%)
6(9.4%)
3(4.7%)
3(4.7%)
0(0.0%)
41(64.1%)
 3493 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_pathological [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_pathological [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_pathological [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
2(3.3%)
9(14.8%)
5(8.2%)
3(4.9%)
3(4.9%)
0(0.0%)
39(63.9%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_pathological [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_pathological [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9
0(0.0%)
0(0.0%)
1(50.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(50.0%)
 1752 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 grade_pathological [factor] 1. Grade_Group_1.1 2. Grade_Group_2.2 3. Grade_Group_3.3 4. Grade_Group_4.4 5. Grade_Group_5.5 6. Not_found_grade_8.8 7. Unknown.9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


NUMBER OF CORES EXAMINED


  • Description: This data item represents the number of cores examined as documented in the pathology report from needle biopsy of the prostate gland.

  • Rationale: Number of Cores Examined is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 13.

  • Codes:

    • 01-99 1 - 99 cores examined(Exact number of cores examined)
    • X1 100 or more cores examined
    • X6 Biopsy cores examined, number unknown
    • X7 No needle core biopsy performed
    • X8 Not applicable: Information not collected for this case(If this information is required by your standard setter, use of code X8 may result in an edit error.)
    • X9 Not documented in medical record/Number of cores examined not assessed or unknown if assessed

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3897


All data

st_css() #IMPORTANT!
  numberofcoresexamined <- trimws(d[,"numberofcoresexamined"])
  numberofcoresexamined[ which(numberofcoresexamined=="09")]<-"9"
  numberofcoresexamined[ which(numberofcoresexamined=="08")]<-"8"
  numberofcoresexamined<-as.factor(numberofcoresexamined)
  levels(numberofcoresexamined) <- list(examined_1_core.1="1",
                                        examined_2_core.2="2",
                                        examined_3_core.3="3",
                                        examined_4_core.4="4",
                                        examined_5_core.5="5",
                                        examined_6_core.6="6",
                                        examined_7_core.7="7",
                                        examined_8_core.8="8",
                                        examined_9_core.9="9",
                                        examined_10_core.10="10",
                                        examined_11_core.11="11",
                                        examined_12_core.12="12",
                                        examined_13_core.13="13",
                                        examined_14_core.14="14",
                                        examined_15_core.15="15",
                                        examined_16_core.16="16",
                                        examined_17_core.17="17",
                                        examined_18_core.18="18",
                                        examined_19_core.19="19",
                                        examined_20_core.20="20",
                                        examined_22_core.22="22",
                                        examined_21_core.21="21",
                                        examined_23_core.23="23",
                                        examined_24_core.24="24",
                                        examined_25_core.25="25",
                                        examined_27_core.27="27",
                                        examined_29_core.29="29",
                                        examined_30_core.30="30",
                                        examined_31_core.31="31",
                                        examined_35_core.35="35",
                                        examined_36_core.36="36",
                                        examined_45_core.45="45",
                                        examined_91_core.91="91",
                                        examined_99_core.99="99",
                                        Biopsy_cores_examined_unknown_number.X6="X6",
                                        No_needle_core_biopsy_performed.X7 = "X7",
                                        Not_documented.X9 = "X9")
    new.d <- data.frame(new.d, numberofcoresexamined)
    new.d <- apply_labels(new.d, numberofcoresexamined = " Gleason score")
    #summary(new.d$numberofcoresexamined)
temp.d <- data.frame (new.d.1, numberofcoresexamined)
summarytools::view(dfSummary(new.d$numberofcoresexamined, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 numberofcoresexamined [labelled, factor] Gleason score 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
36(57.1%)
2(3.2%)
4(6.3%)
1(1.6%)
1(1.6%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
11(17.5%)
3(4.8%)
1(1.6%)
 3494 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
34(55.7%)
2(3.3%)
4(6.6%)
1(1.6%)
1(1.6%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
11(18.0%)
3(4.9%)
1(1.6%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_examined [factor] 1. examined_1_core.1 2. examined_2_core.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


NUMBER OF CORES POSITIVE


  • Description:This data item represents the number of positive cores documented in the pathology report from needle biopsy of the prostate gland.

  • Rationale: Number of Cores Positive is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 12.

  • Codes

    • 00: All examined cores negative
    • 01-99: 1 - 99 cores positive (Exact number of cores positive)
    • X1: 100 or more cores positive
    • X6: Biopsy cores positive, number unknown
    • X7: No needle core biopsy performed
    • X8: Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
    • X9: Not documented in medical record/Number of Cores Positive not assessed or unknown if assessed

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3898


All data

st_css() #IMPORTANT!
  numberofcorespositive <- trimws(d[,"numberofcorespositive"])
  numberofcorespositive[ which(numberofcorespositive=="02")]<-"2"
  numberofcorespositive[ which(numberofcorespositive=="04")]<-"4"
  numberofcorespositive[ which(numberofcorespositive=="05")]<-"5"
  numberofcorespositive[ which(numberofcorespositive=="01")]<-"1"
  numberofcorespositive[ which(numberofcorespositive=="07")]<-"7"
  numberofcorespositive[ which(numberofcorespositive=="09")]<-"9"
  numberofcorespositive[ which(numberofcorespositive=="08")]<-"8"
  numberofcorespositive[ which(numberofcorespositive=="03")]<-"3"
  numberofcorespositive<-as.factor(numberofcorespositive)
  levels(numberofcorespositive) <- list(examined_1_core.1="1",
                                        examined_2_cores.2="2",
                                        examined_3_core.3="3",
                                        examined_4_core.4="4",
                                        examined_5_core.5="5",
                                        examined_6_core.6="6",
                                        examined_7_core.7="7",
                                        examined_8_core.8="8",
                                        examined_9_core.9="9",
                                        examined_10_core.10="10",
                                        examined_11_core.11="11",
                                        examined_12_core.12="12",
                                        examined_13_core.13="13",
                                        examined_14_core.14="14",
                                        examined_15_core.15="15",
                                        examined_16_core.16="16",
                                        examined_17_core.17="17",
                                        examined_18_core.18="18",
                                        examined_19_core.19="19",
                                        examined_20_core.20="20",
                                        examined_22_core.22="22",
                                        examined_21_core.21="21",
                                        examined_23_core.23="23",
                                        examined_24_core.24="24",
                                        examined_25_core.25="25",
                                        examined_27_core.27="27",
                                        examined_29_core.29="29",
                                        examined_30_core.30="30",
                                        examined_31_core.31="31",
                                        examined_35_core.35="35",
                                        examined_36_core.36="36",
                                        examined_45_core.45="45",
                                        examined_91_core.91="91",
                                        examined_99_core.99="99",
                                        Biopsy_cores_examined_unknown_number.X6="X6",
                                        No_needle_core_biopsy_performed.X7 = "X7",
                                        Not_applicable.X8 = "X8",
                                        Not_documented.X9 = "X9")
    new.d <- data.frame(new.d, numberofcorespositive)
    new.d <- apply_labels(new.d, numberofcorespositive = " Gleason score")
    #summary(new.d$numberofcorespositive)
temp.d <- data.frame (new.d.1, numberofcorespositive)
summarytools::view(dfSummary(new.d$numberofcorespositive, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 numberofcorespositive [labelled, factor] Gleason score 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_applicable.X8 38. Not_documented.X9
6(9.7%)
9(14.5%)
6(9.7%)
8(12.9%)
13(21.0%)
0(0.0%)
3(4.8%)
3(4.8%)
2(3.2%)
3(4.8%)
0(0.0%)
2(3.2%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(6.5%)
3(4.8%)
0(0.0%)
0(0.0%)
 3495 (98.3%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_applicable.X8 38. Not_documented.X9 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_applicable.X8 38. Not_documented.X9 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_applicable.X8 38. Not_documented.X9 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_applicable.X8 38. Not_documented.X9
6(10.0%)
8(13.3%)
6(10.0%)
7(11.7%)
13(21.7%)
0(0.0%)
3(5.0%)
3(5.0%)
2(3.3%)
3(5.0%)
0(0.0%)
2(3.3%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
4(6.7%)
3(5.0%)
0(0.0%)
0(0.0%)
 296 (83.1%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_applicable.X8 38. Not_documented.X9
0(0.0%)
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unk 36. No_needle_core_biopsy_per 37. Not_applicable.X8 38. Not_documented.X9
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 number_of_cores_positive [factor] 1. examined_1_core.1 2. examined_2_cores.2 3. examined_3_core.3 4. examined_4_core.4 5. examined_5_core.5 6. examined_6_core.6 7. examined_7_core.7 8. examined_8_core.8 9. examined_9_core.9 10. examined_10_core.10 11. examined_11_core.11 12. examined_12_core.12 13. examined_13_core.13 14. examined_14_core.14 15. examined_15_core.15 16. examined_16_core.16 17. examined_17_core.17 18. examined_18_core.18 19. examined_19_core.19 20. examined_20_core.20 21. examined_22_core.22 22. examined_21_core.21 23. examined_23_core.23 24. examined_24_core.24 25. examined_25_core.25 26. examined_27_core.27 27. examined_29_core.29 28. examined_30_core.30 29. examined_31_core.31 30. examined_35_core.35 31. examined_36_core.36 32. examined_45_core.45 33. examined_91_core.91 34. examined_99_core.99 35. Biopsy_cores_examined_unknown_number.X6 36. No_needle_core_biopsy_performed.X7 37. Not_applicable.X8 38. Not_documented.X9 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


EOD PROSTATE PATHOLOGIC EXTENSION


  • Description: EOD Prostate Pathologic Extension is used to assign pT category for prostate cancer based on radical prostatectomy specimens.

  • Rationale: EOD Prostate Pathologic Extension is used in EOD. It was previously collected as Prostate Pathological Extension, and Prostate, CS SSF# 3.

  • Codes (See the most current version of EOD (Prostate) (https://staging.seer.cancer.gov/) for rules and site-specific codes and coding structures.)

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3919


All data

st_css() #IMPORTANT!
  prostatepathologicalextension <- as.factor(trimws(d[,"prostatepathologicalextension"]))
  levels(prostatepathologicalextension) <- list(T2.300="300",
                                T4_600.600="600",
                                TX_900.900="900", 
                                T3.500="500",
                                T3a.350="350",
                                T3b.400="400",
                                T4_700.700 = "700",
                                TX_950.950 = "950",
                                In_situ_88.0 = "0",
                                T0.800 = "800",
                                Not_found_250.250 = "250"
                                )
    new.d <- data.frame(new.d, prostatepathologicalextension)
    new.d <- apply_labels(new.d, prostatepathologicalextension = "Grade of primary tumor before any treatment")
    #summary(new.d$prostatepathologicalextension)
temp.d <- data.frame (new.d.1, prostatepathologicalextension)
summarytools::view(dfSummary(new.d$prostatepathologicalextension, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 prostatepathologicalextension [labelled, factor] Grade of primary tumor before any treatment 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250
15(23.8%)
0(0.0%)
40(63.5%)
0(0.0%)
4(6.3%)
3(4.8%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
 3494 (98.2%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250 All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250 All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250 All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250
15(24.6%)
0(0.0%)
38(62.3%)
0(0.0%)
4(6.6%)
3(4.9%)
0(0.0%)
1(1.6%)
0(0.0%)
0(0.0%)
0(0.0%)
 295 (82.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250
0(0.0%)
0(0.0%)
1(100.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
0(0.0%)
 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 prostate_pathological_extension [factor] 1. T2.300 2. T4_600.600 3. TX_900.900 4. T3.500 5. T3a.350 6. T3b.400 7. T4_700.700 8. TX_950.950 9. In_situ_88.0 10. T0.800 11. Not_found_250.250 All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07


PSA (PROSTATIC SPECIFIC ANTIGEN) LAB VALUE


  • Description: PSA (Prostatic Specific Antigen) is a protein produced by cells of the prostate gland and is elevated in patients with prostate cancer. This data item pertains to PSA lab value.

  • Rationale: This data item is required for prognostic stage grouping in AJCC 8th edition, Chapter 58, Prostate. It was previously collected as Prostate, CS SSF# 1.

  • Codes

    • 0.1 0.1 or less nanograms/milliliter (ng/ml) (Exact value to nearest tenth of ng/ml)
    • 0.2-999.9 0.2–999.9 ng/ml (Exact value to nearest tenth of ng/ml)
    • XXX.1 1,000 ng/ml or greater
    • XXX.7 Test ordered, results not in chart
    • XXX.9 Not documented in medical record/PSA lab value not assessed or unknown if assessed

  • Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.

  • Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3920


All data

st_css() #IMPORTANT!
  psalabvalue <- trimws(d[,"psalabvalue"])
  #psalabvalue[ which(psalabvalue=="XXX.1")]<-"10001"
  #psalabvalue[ which(psalabvalue=="XXX.7")]<-"10007"
  #psalabvalue[ which(psalabvalue=="XXX.9")]<-"10009"
  #psalabvalue<-as.factor(psalabvalue)
  
  psalabvalue <- ifelse(psalabvalue=="XXX.9", NA,
                           ifelse(psalabvalue=="XXX.7", NA,
                                  ifelse(psalabvalue=="XXX.1", 1000, psalabvalue)))
  psalabvalue <- as.numeric(psalabvalue)
    
    new.d <- data.frame(new.d, psalabvalue)
    new.d <- apply_labels(new.d, psalabvalue = "PSA_lab_value")
    #summary(new.d$psalabvalue)
temp.d <- data.frame (new.d.1, psalabvalue)
summarytools::view(dfSummary(new.d$psalabvalue, style = 'grid', max.distinct.values = 1000, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 psalabvalue [labelled, numeric] PSA_lab_value Mean (sd) : 37.6 (156.1) min < med < max: 2.4 < 6 < 1000 IQR (CV) : 6.8 (4.1) 45 distinct values 3501 (98.4%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

LA County

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] All NA's 321 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Northern CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] All NA's 210 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Greater CA

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] All NA's 315 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Detroit

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] Mean (sd) : 38.9 (158.8) min < med < max: 2.9 < 6.2 < 1000 IQR (CV) : 6.8 (4.1) 43 distinct values 302 (84.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Louisiana

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] 1 distinct value 1 distinct values 584 (99.8%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Georgia

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] 1 distinct value 1 distinct values 1753 (99.9%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07

Michigan

No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 psa_lab_value [numeric] All NA's 16 (100.0%)

Generated by summarytools 0.9.8 (R version 4.0.3)
2021-06-07